References

Ansaetze

from qml_essentials.ansaetze import Ansaetze

Source code in qml_essentials/ansaetze.py

class Ansaetze:
    def get_available():
        return [
            Ansaetze.No_Ansatz,
            Ansaetze.Circuit_1,
            Ansaetze.Circuit_2,
            Ansaetze.Circuit_3,
            Ansaetze.Circuit_4,
            Ansaetze.Circuit_6,
            Ansaetze.Circuit_9,
            Ansaetze.Circuit_10,
            Ansaetze.Circuit_15,
            Ansaetze.Circuit_16,
            Ansaetze.Circuit_17,
            Ansaetze.Circuit_18,
            Ansaetze.Circuit_19,
            Ansaetze.No_Entangling,
            Ansaetze.Strongly_Entangling,
            Ansaetze.Hardware_Efficient,
            Ansaetze.GHZ,
        ]

    class No_Ansatz(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            return 0

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            return 0

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            pass

    class GHZ(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            return 0

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for the GHZ circuit.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Total number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("H")
            n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            Gates.H(0, **kwargs)

            for q in range(n_qubits - 1):
                Gates.CX([q, q + 1], **kwargs)

    class Hardware_Efficient(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the
            Hardware Efficient Ansatz.

            The number of parameters is 3 times the number of qubits when there
            is more than one qubit, as each qubit contributes 3 parameters.
            If the number of qubits is less than 2, a warning is logged since
            no entanglement is possible, and a fixed number of 2 parameters is used.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of parameters required for one layer of the circuit.
            """
            if n_qubits < 2:
                log.warning("Number of Qubits < 2, no entanglement available")
            return n_qubits * 3

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for the
            Hardware Efficient Ansatz.

            This counts all parameters needed if the circuit is used at the
            pulse level. It includes contributions from single-qubit rotations
            (`RY` and `RZ`) and multi-qubit gates (`CX`) if more than one qubit
            is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = 2 * PulseInformation.num_params("RY")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_CX = (n_qubits // 2) + ((n_qubits - 1) // 2)
            n_CX += 1 if n_qubits > 2 else 0
            n_params += n_CX * PulseInformation.num_params("CX")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Hardware-Efficient ansatz, as proposed in
            https://arxiv.org/pdf/2309.03279

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits // 2):
                    Gates.CX(wires=[(2 * q), (2 * q + 1)], **kwargs)
                for q in range((n_qubits - 1) // 2):
                    Gates.CX(wires=[(2 * q + 1), (2 * q + 2)], **kwargs)
                if n_qubits > 2:
                    Gates.CX(wires=[(n_qubits - 1), 0], **kwargs)

    class Circuit_19(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_19.

            The number of parameters is 3 times the number of qubits when there
            is more than one qubit, as each qubit contributes 3 parameters.
            If the number of qubits is less than 2, a warning is logged since
            no entanglement is possible, and a fixed number of 2 parameters is used.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters required for one layer of the circuit
            """
            if n_qubits > 1:
                return n_qubits * 3
            else:
                log.warning("Number of Qubits < 2, no entanglement available")
                return 2

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_19.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRX`) on each qubit if more than one
            qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            if n_qubits > 1:
                n_params += PulseInformation.num_params("CRX") * n_qubits

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            Returns the indices for the controlled rotation gates for one layer.
            Indices should slice the list of all parameters for one layer as follows:
            [indices[0]:indices[1]:indices[2]]

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-n_qubits, None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit19 ansatz.

            Length of flattened vector must be n_qubits*3
            because for >1 qubits there are three gates

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CRX(
                        w[w_idx],
                        wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                        **kwargs,
                    )
                    w_idx += 1

    class Circuit_18(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_18.

            The number of parameters is 3 times the number of qubits when there
            is more than one qubit, as each qubit contributes 3 parameters.
            If the number of qubits is less than 2, a warning is logged since
            no entanglement is possible, and a fixed number of 2 parameters is used.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters required for one layer of the circuit
            """
            if n_qubits > 1:
                return n_qubits * 3
            else:
                log.warning("Number of Qubits < 2, no entanglement available")
                return 2

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_18.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRZ`) on each qubit if more than one
            qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            if n_qubits > 1:
                n_params += PulseInformation.num_params("CRZ") * n_qubits

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            Returns the indices for the controlled rotation gates for one layer.
            Indices should slice the list of all parameters for one layer as follows:
            [indices[0]:indices[1]:indices[2]]

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-n_qubits, None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit18 ansatz.

            Length of flattened vector must be n_qubits*3

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CRZ(
                        w[w_idx],
                        wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                        **kwargs,
                    )
                    w_idx += 1

    class Circuit_15(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_15.

            The number of parameters is 2 times the number of qubits.
            A warning is logged if the number of qubits is less than 2.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters required for one layer of the circuit
            """
            if n_qubits > 1:
                return n_qubits * 2
            else:
                log.warning("Number of Qubits < 2, no entanglement available")
                return 2

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_15.

            This includes contributions from single-qubit rotations (`RY`) on all
            qubits, and controlled rotations (`CX`) on each qubit if more than one
            qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = 2 * PulseInformation.num_params("RY")
            n_params *= n_qubits

            if n_qubits > 1:
                n_params += PulseInformation.num_params("CX") * n_qubits

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit15 ansatz.

            Length of flattened vector must be n_qubits*2
            because for >1 qubits there are three gates

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*2
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CX(
                        wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                        **kwargs,
                    )

            for q in range(n_qubits):
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CX(
                        wires=[(q - 1) % n_qubits, (q - 2) % n_qubits],
                        **kwargs,
                    )

    class Circuit_9(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_9.

            The number of parameters is equal to the number of qubits.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters required for one layer of the circuit
            """
            return n_qubits

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_9.

            This includes contributions from single-qubit rotations (`H`, `RX`) on all
            qubits, and controlled rotations (`CZ`) on each qubit except one if more
            than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("H")
            n_params += PulseInformation.num_params("RX")
            n_params *= n_qubits

            n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit9 ansatz.

            Length of flattened vector must be n_qubits

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.H(wires=q, **kwargs)

            for q in range(n_qubits - 1):
                Gates.CZ(
                    wires=[n_qubits - q - 2, n_qubits - q - 1],
                    **kwargs,
                )

            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1

    class Circuit_6(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_6.

            The total number of parameters is n_qubits*3+n_qubits**2, which is
            the number of rotations n_qubits*3 plus the number of entangling gates
            n_qubits**2.

            If n_qubits is 1, the number of parameters is 4, and a warning is logged
            since no entanglement is possible.

            Parameters
            ----------
            n_qubits : int
                Number of qubits

            Returns
            -------
            int
                Number of parameters per layer
            """
            if n_qubits > 1:
                return n_qubits * 3 + n_qubits**2
            else:
                log.warning("Number of Qubits < 2, no entanglement available")
                return 4

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_6.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRX`) on each qubit twice except repeats
            if more than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = 2 * PulseInformation.num_params("RX")
            n_params += 2 * PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_CRX = n_qubits * (n_qubits - 1)
            n_params += n_CRX * PulseInformation.num_params("CRX")

            return 0

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            Returns the indices for the controlled rotation gates for one layer.
            Indices should slice the list of all parameters for one layer as follows:
            [indices[0]:indices[1]:indices[2]]

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            # TODO: implement
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit6 ansatz.

            Length of flattened vector must be
                n_qubits*4+n_qubits*(n_qubits-1) =
                n_qubits*3+n_qubits**2

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size
                    n_layers*(n_qubits*3+n_qubits**2)
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for ql in range(n_qubits):
                    for q in range(n_qubits):
                        if q == ql:
                            continue
                        Gates.CRX(
                            w[w_idx],
                            wires=[n_qubits - ql - 1, (n_qubits - q - 1) % n_qubits],
                            **kwargs,
                        )
                        w_idx += 1

            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

    class Circuit_1(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_1.

            The total number of parameters is determined by the number of qubits, with
            each qubit contributing 2 parameters.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 2

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_9.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits only.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit1 ansatz.

            Length of flattened vector must be n_qubits*2

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*2
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

    class Circuit_2(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for Circuit_2.

            The total number of parameters is determined by the number of qubits, with
            each qubit contributing 2 parameters.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 2

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_2.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CX`) on each qubit except one if more
            than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            if n_qubits > 1:
                n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

            return 0

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit2 ansatz.

            Length of flattened vector must be n_qubits*2

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*2
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            for q in range(n_qubits - 1):
                Gates.CX(
                    wires=[n_qubits - q - 1, n_qubits - q - 2],
                    **kwargs,
                )

    class Circuit_3(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Calculates the number of parameters per layer for Circuit3.

            The number of parameters per layer is given by the number of qubits, with
            each qubit contributing 3 parameters. The last qubit only contributes 2
            parameters because it is the target qubit for the controlled gates.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 3 - 1

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_3.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRZ`) on each qubit except one if more
            than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params = PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_params += (n_qubits - 1) * PulseInformation.num_params("CRZ")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-(n_qubits - 1), None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit3 ansatz.

            Length of flattened vector must be n_qubits*3-1

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3-1
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            for q in range(n_qubits - 1):
                Gates.CRZ(
                    w[w_idx],
                    wires=[n_qubits - q - 1, n_qubits - q - 2],
                    **kwargs,
                )
                w_idx += 1

    class Circuit_4(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the Circuit_4 ansatz.

            The number of parameters is calculated as n_qubits*3-1.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 3 - 1

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_4.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRX`) on each qubit except one if more
            than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_params += (n_qubits - 1) * PulseInformation.num_params("CRX")

            return 0

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-(n_qubits - 1), None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit4 ansatz.

            Length of flattened vector must be n_qubits*3-1

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3-1
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            for q in range(n_qubits - 1):
                Gates.CRX(
                    w[w_idx],
                    wires=[n_qubits - q - 1, n_qubits - q - 2],
                    **kwargs,
                )
                w_idx += 1

    class Circuit_10(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the Circuit_10 ansatz.

            The number of parameters is calculated as n_qubits*2.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 2  # constant gates not considered yet. has to be fixed

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_10.

            This includes contributions from single-qubit rotations (`RY`) on all
            qubits, controlled rotations (`CZ`) on each qubit except one if more
            than one qubit is present and a final controlled rotation (`CZ`) if
            more than two qubits are present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = 2 * PulseInformation.num_params("RY")
            n_params *= n_qubits

            n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

            n_params += PulseInformation.num_params("CZ") if n_qubits > 2 else 0

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit10 ansatz.

            Length of flattened vector must be n_qubits*2

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*2
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            # constant gates, independent of layers. has to be fixed
            for q in range(n_qubits):
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            for q in range(n_qubits - 1):
                Gates.CZ(
                    wires=[
                        (n_qubits - q - 2) % n_qubits,
                        (n_qubits - q - 1) % n_qubits,
                    ],
                    **kwargs,
                )
            if n_qubits > 2:
                Gates.CZ(wires=[n_qubits - 1, 0], **kwargs)

            for q in range(n_qubits):
                Gates.RY(w[w_idx], wires=q, **kwargs)
                w_idx += 1

    class Circuit_16(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the Circuit_16 ansatz.

            The number of parameters is calculated as n_qubits*3-1.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 3 - 1

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_16.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRZ`) if more than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_CRZ = n_qubits * (n_qubits - 1) // 2
            n_params += n_CRZ * PulseInformation.num_params("CRZ")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-(n_qubits - 1), None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit16 ansatz.

            Length of flattened vector must be n_qubits*3-1

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3-1
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits // 2):
                    Gates.CRZ(
                        w[w_idx],
                        wires=[(2 * q + 1), (2 * q)],
                        **kwargs,
                    )
                    w_idx += 1

                for q in range((n_qubits - 1) // 2):
                    Gates.CRZ(
                        w[w_idx],
                        wires=[(2 * q + 2), (2 * q + 1)],
                        **kwargs,
                    )
                    w_idx += 1

    class Circuit_17(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the Circuit_17 ansatz.

            The number of parameters is calculated as n_qubits*3-1.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 3 - 1

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Circuit_17.

            This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
            qubits, and controlled rotations (`CRX`) if more than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("RX")
            n_params += PulseInformation.num_params("RZ")
            n_params *= n_qubits

            n_CRZ = n_qubits * (n_qubits - 1) // 2
            n_params += n_CRZ * PulseInformation.num_params("CRX")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            if n_qubits > 1:
                return [-(n_qubits - 1), None, None]
            else:
                return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a Circuit17 ansatz.

            Length of flattened vector must be n_qubits*3-1

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3-1
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.RX(w[w_idx], wires=q, **kwargs)
                w_idx += 1
                Gates.RZ(w[w_idx], wires=q, **kwargs)
                w_idx += 1

            if n_qubits > 1:
                for q in range(n_qubits // 2):
                    Gates.CRX(
                        w[w_idx],
                        wires=[(2 * q + 1), (2 * q)],
                        **kwargs,
                    )
                    w_idx += 1

                for q in range((n_qubits - 1) // 2):
                    Gates.CRX(
                        w[w_idx],
                        wires=[(2 * q + 2), (2 * q + 1)],
                        **kwargs,
                    )
                    w_idx += 1

    class Strongly_Entangling(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the
            Strongly Entangling ansatz.

            The number of parameters is calculated as n_qubits*6.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            if n_qubits < 2:
                log.warning("Number of Qubits < 2, no entanglement available")
            return n_qubits * 6

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for Strongly_Entangling
            circuit.

            This includes contributions from single-qubit rotations (`Rot`) on all
            qubits, and controlled rotations (`CX`) if more than one qubit is present.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = 2 * PulseInformation.num_params("Rot")
            n_params *= n_qubits

            if n_qubits > 1:
                n_params += n_qubits * 2 * PulseInformation.num_params("CX")

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs) -> None:
            """
            Creates a Strongly Entangling ansatz.

            Length of flattened vector must be n_qubits*6

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*6
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.Rot(
                    w[w_idx],
                    w[w_idx + 1],
                    w[w_idx + 2],
                    wires=q,
                    **kwargs,
                )
                w_idx += 3

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CX(wires=[q, (q + 1) % n_qubits], **kwargs)

            for q in range(n_qubits):
                Gates.Rot(
                    w[w_idx],
                    w[w_idx + 1],
                    w[w_idx + 2],
                    wires=q,
                    **kwargs,
                )
                w_idx += 3

            if n_qubits > 1:
                for q in range(n_qubits):
                    Gates.CX(
                        wires=[q, (q + n_qubits // 2) % n_qubits],
                        **kwargs,
                    )

    class No_Entangling(Circuit):
        @staticmethod
        def n_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of parameters per layer for the NoEntangling ansatz.

            The number of parameters is calculated as n_qubits*3.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            int
                Number of parameters per layer
            """
            return n_qubits * 3

        @staticmethod
        def n_pulse_params_per_layer(n_qubits: int) -> int:
            """
            Returns the number of pulse parameters per layer for No_Entangling circuit.

            This includes contributions from single-qubit rotations (`Rot`) on all
            qubits only.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit.

            Returns
            -------
            int
                Number of pulse parameters required for one layer of the circuit.
            """
            n_params = PulseInformation.num_params("Rot")
            n_params *= n_qubits

            return n_params

        @staticmethod
        def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
            """
            No controlled rotation gates available. Always None.

            Parameters
            ----------
            n_qubits : int
                Number of qubits in the circuit

            Returns
            -------
            Optional[np.ndarray]
                List of all controlled indices, or None if the circuit does not
                contain controlled rotation gates.
            """
            return None

        @staticmethod
        def build(w: np.ndarray, n_qubits: int, **kwargs):
            """
            Creates a circuit without entangling, but with U3 gates on all qubits

            Length of flattened vector must be n_qubits*3

            Parameters
            ----------
            w : np.ndarray
                Weight vector of size n_qubits*3
            n_qubits : int
                Number of qubits
            noise_params : Optional[Dict[str, float]], optional
                Dictionary of noise parameters to apply to the gates
            """
            w_idx = 0
            for q in range(n_qubits):
                Gates.Rot(
                    w[w_idx],
                    w[w_idx + 1],
                    w[w_idx + 2],
                    wires=q,
                    **kwargs,
                )
                w_idx += 3

Circuit_1

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_1(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_1.

        The total number of parameters is determined by the number of qubits, with
        each qubit contributing 2 parameters.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 2

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_9.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits only.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit1 ansatz.

        Length of flattened vector must be n_qubits*2

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*2
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit1 ansatz.

Length of flattened vector must be n_qubits*2

Parameters

w : np.ndarray Weight vector of size n_qubits*2 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit1 ansatz.

    Length of flattened vector must be n_qubits*2

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*2
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_1.

The total number of parameters is determined by the number of qubits, with each qubit contributing 2 parameters.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_1.

    The total number of parameters is determined by the number of qubits, with
    each qubit contributing 2 parameters.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 2

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_9.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits only.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_9.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits only.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    return n_params

Circuit_10

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_10(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the Circuit_10 ansatz.

        The number of parameters is calculated as n_qubits*2.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 2  # constant gates not considered yet. has to be fixed

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_10.

        This includes contributions from single-qubit rotations (`RY`) on all
        qubits, controlled rotations (`CZ`) on each qubit except one if more
        than one qubit is present and a final controlled rotation (`CZ`) if
        more than two qubits are present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = 2 * PulseInformation.num_params("RY")
        n_params *= n_qubits

        n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

        n_params += PulseInformation.num_params("CZ") if n_qubits > 2 else 0

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit10 ansatz.

        Length of flattened vector must be n_qubits*2

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*2
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        # constant gates, independent of layers. has to be fixed
        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        for q in range(n_qubits - 1):
            Gates.CZ(
                wires=[
                    (n_qubits - q - 2) % n_qubits,
                    (n_qubits - q - 1) % n_qubits,
                ],
                **kwargs,
            )
        if n_qubits > 2:
            Gates.CZ(wires=[n_qubits - 1, 0], **kwargs)

        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit10 ansatz.

Length of flattened vector must be n_qubits*2

Parameters

w : np.ndarray Weight vector of size n_qubits*2 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit10 ansatz.

    Length of flattened vector must be n_qubits*2

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*2
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    # constant gates, independent of layers. has to be fixed
    for q in range(n_qubits):
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    for q in range(n_qubits - 1):
        Gates.CZ(
            wires=[
                (n_qubits - q - 2) % n_qubits,
                (n_qubits - q - 1) % n_qubits,
            ],
            **kwargs,
        )
    if n_qubits > 2:
        Gates.CZ(wires=[n_qubits - 1, 0], **kwargs)

    for q in range(n_qubits):
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Circuit_10 ansatz.

The number of parameters is calculated as n_qubits*2.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the Circuit_10 ansatz.

    The number of parameters is calculated as n_qubits*2.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 2  # constant gates not considered yet. has to be fixed

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_10.

This includes contributions from single-qubit rotations (RY) on all qubits, controlled rotations (CZ) on each qubit except one if more than one qubit is present and a final controlled rotation (CZ) if more than two qubits are present.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_10.

    This includes contributions from single-qubit rotations (`RY`) on all
    qubits, controlled rotations (`CZ`) on each qubit except one if more
    than one qubit is present and a final controlled rotation (`CZ`) if
    more than two qubits are present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = 2 * PulseInformation.num_params("RY")
    n_params *= n_qubits

    n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

    n_params += PulseInformation.num_params("CZ") if n_qubits > 2 else 0

    return n_params

Circuit_15

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_15(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_15.

        The number of parameters is 2 times the number of qubits.
        A warning is logged if the number of qubits is less than 2.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters required for one layer of the circuit
        """
        if n_qubits > 1:
            return n_qubits * 2
        else:
            log.warning("Number of Qubits < 2, no entanglement available")
            return 2

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_15.

        This includes contributions from single-qubit rotations (`RY`) on all
        qubits, and controlled rotations (`CX`) on each qubit if more than one
        qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = 2 * PulseInformation.num_params("RY")
        n_params *= n_qubits

        if n_qubits > 1:
            n_params += PulseInformation.num_params("CX") * n_qubits

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit15 ansatz.

        Length of flattened vector must be n_qubits*2
        because for >1 qubits there are three gates

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*2
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CX(
                    wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                    **kwargs,
                )

        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CX(
                    wires=[(q - 1) % n_qubits, (q - 2) % n_qubits],
                    **kwargs,
                )

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit15 ansatz.

Length of flattened vector must be n_qubits*2 because for >1 qubits there are three gates

Parameters

w : np.ndarray Weight vector of size n_qubits*2 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit15 ansatz.

    Length of flattened vector must be n_qubits*2
    because for >1 qubits there are three gates

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*2
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CX(
                wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                **kwargs,
            )

    for q in range(n_qubits):
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CX(
                wires=[(q - 1) % n_qubits, (q - 2) % n_qubits],
                **kwargs,
            )

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_15.

The number of parameters is 2 times the number of qubits. A warning is logged if the number of qubits is less than 2.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters required for one layer of the circuit

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_15.

    The number of parameters is 2 times the number of qubits.
    A warning is logged if the number of qubits is less than 2.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters required for one layer of the circuit
    """
    if n_qubits > 1:
        return n_qubits * 2
    else:
        log.warning("Number of Qubits < 2, no entanglement available")
        return 2

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_15.

This includes contributions from single-qubit rotations (RY) on all qubits, and controlled rotations (CX) on each qubit if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_15.

    This includes contributions from single-qubit rotations (`RY`) on all
    qubits, and controlled rotations (`CX`) on each qubit if more than one
    qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = 2 * PulseInformation.num_params("RY")
    n_params *= n_qubits

    if n_qubits > 1:
        n_params += PulseInformation.num_params("CX") * n_qubits

    return n_params

Circuit_16

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_16(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the Circuit_16 ansatz.

        The number of parameters is calculated as n_qubits*3-1.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 3 - 1

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_16.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRZ`) if more than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_CRZ = n_qubits * (n_qubits - 1) // 2
        n_params += n_CRZ * PulseInformation.num_params("CRZ")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-(n_qubits - 1), None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit16 ansatz.

        Length of flattened vector must be n_qubits*3-1

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3-1
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits // 2):
                Gates.CRZ(
                    w[w_idx],
                    wires=[(2 * q + 1), (2 * q)],
                    **kwargs,
                )
                w_idx += 1

            for q in range((n_qubits - 1) // 2):
                Gates.CRZ(
                    w[w_idx],
                    wires=[(2 * q + 2), (2 * q + 1)],
                    **kwargs,
                )
                w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit16 ansatz.

Length of flattened vector must be n_qubits*3-1

Parameters

w : np.ndarray Weight vector of size n_qubits*3-1 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit16 ansatz.

    Length of flattened vector must be n_qubits*3-1

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3-1
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits // 2):
            Gates.CRZ(
                w[w_idx],
                wires=[(2 * q + 1), (2 * q)],
                **kwargs,
            )
            w_idx += 1

        for q in range((n_qubits - 1) // 2):
            Gates.CRZ(
                w[w_idx],
                wires=[(2 * q + 2), (2 * q + 1)],
                **kwargs,
            )
            w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-(n_qubits - 1), None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Circuit_16 ansatz.

The number of parameters is calculated as n_qubits*3-1.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the Circuit_16 ansatz.

    The number of parameters is calculated as n_qubits*3-1.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 3 - 1

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_16.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRZ) if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_16.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRZ`) if more than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_CRZ = n_qubits * (n_qubits - 1) // 2
    n_params += n_CRZ * PulseInformation.num_params("CRZ")

    return n_params

Circuit_17

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_17(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the Circuit_17 ansatz.

        The number of parameters is calculated as n_qubits*3-1.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 3 - 1

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_17.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRX`) if more than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_CRZ = n_qubits * (n_qubits - 1) // 2
        n_params += n_CRZ * PulseInformation.num_params("CRX")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-(n_qubits - 1), None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit17 ansatz.

        Length of flattened vector must be n_qubits*3-1

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3-1
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits // 2):
                Gates.CRX(
                    w[w_idx],
                    wires=[(2 * q + 1), (2 * q)],
                    **kwargs,
                )
                w_idx += 1

            for q in range((n_qubits - 1) // 2):
                Gates.CRX(
                    w[w_idx],
                    wires=[(2 * q + 2), (2 * q + 1)],
                    **kwargs,
                )
                w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit17 ansatz.

Length of flattened vector must be n_qubits*3-1

Parameters

w : np.ndarray Weight vector of size n_qubits*3-1 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit17 ansatz.

    Length of flattened vector must be n_qubits*3-1

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3-1
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits // 2):
            Gates.CRX(
                w[w_idx],
                wires=[(2 * q + 1), (2 * q)],
                **kwargs,
            )
            w_idx += 1

        for q in range((n_qubits - 1) // 2):
            Gates.CRX(
                w[w_idx],
                wires=[(2 * q + 2), (2 * q + 1)],
                **kwargs,
            )
            w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-(n_qubits - 1), None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Circuit_17 ansatz.

The number of parameters is calculated as n_qubits*3-1.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the Circuit_17 ansatz.

    The number of parameters is calculated as n_qubits*3-1.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 3 - 1

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_17.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRX) if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_17.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRX`) if more than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_CRZ = n_qubits * (n_qubits - 1) // 2
    n_params += n_CRZ * PulseInformation.num_params("CRX")

    return n_params

Circuit_18

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_18(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_18.

        The number of parameters is 3 times the number of qubits when there
        is more than one qubit, as each qubit contributes 3 parameters.
        If the number of qubits is less than 2, a warning is logged since
        no entanglement is possible, and a fixed number of 2 parameters is used.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters required for one layer of the circuit
        """
        if n_qubits > 1:
            return n_qubits * 3
        else:
            log.warning("Number of Qubits < 2, no entanglement available")
            return 2

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_18.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRZ`) on each qubit if more than one
        qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        if n_qubits > 1:
            n_params += PulseInformation.num_params("CRZ") * n_qubits

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        Returns the indices for the controlled rotation gates for one layer.
        Indices should slice the list of all parameters for one layer as follows:
        [indices[0]:indices[1]:indices[2]]

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-n_qubits, None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit18 ansatz.

        Length of flattened vector must be n_qubits*3

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CRZ(
                    w[w_idx],
                    wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                    **kwargs,
                )
                w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit18 ansatz.

Length of flattened vector must be n_qubits*3

Parameters

w : np.ndarray Weight vector of size n_qubits*3 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit18 ansatz.

    Length of flattened vector must be n_qubits*3

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CRZ(
                w[w_idx],
                wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                **kwargs,
            )
            w_idx += 1

get_control_indices(n_qubits) staticmethod

Returns the indices for the controlled rotation gates for one layer. Indices should slice the list of all parameters for one layer as follows: [indices[0]:indices[1]:indices[2]]

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    Returns the indices for the controlled rotation gates for one layer.
    Indices should slice the list of all parameters for one layer as follows:
    [indices[0]:indices[1]:indices[2]]

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-n_qubits, None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_18.

The number of parameters is 3 times the number of qubits when there is more than one qubit, as each qubit contributes 3 parameters. If the number of qubits is less than 2, a warning is logged since no entanglement is possible, and a fixed number of 2 parameters is used.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters required for one layer of the circuit

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_18.

    The number of parameters is 3 times the number of qubits when there
    is more than one qubit, as each qubit contributes 3 parameters.
    If the number of qubits is less than 2, a warning is logged since
    no entanglement is possible, and a fixed number of 2 parameters is used.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters required for one layer of the circuit
    """
    if n_qubits > 1:
        return n_qubits * 3
    else:
        log.warning("Number of Qubits < 2, no entanglement available")
        return 2

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_18.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRZ) on each qubit if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_18.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRZ`) on each qubit if more than one
    qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    if n_qubits > 1:
        n_params += PulseInformation.num_params("CRZ") * n_qubits

    return n_params

Circuit_19

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_19(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_19.

        The number of parameters is 3 times the number of qubits when there
        is more than one qubit, as each qubit contributes 3 parameters.
        If the number of qubits is less than 2, a warning is logged since
        no entanglement is possible, and a fixed number of 2 parameters is used.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters required for one layer of the circuit
        """
        if n_qubits > 1:
            return n_qubits * 3
        else:
            log.warning("Number of Qubits < 2, no entanglement available")
            return 2

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_19.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRX`) on each qubit if more than one
        qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        if n_qubits > 1:
            n_params += PulseInformation.num_params("CRX") * n_qubits

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        Returns the indices for the controlled rotation gates for one layer.
        Indices should slice the list of all parameters for one layer as follows:
        [indices[0]:indices[1]:indices[2]]

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-n_qubits, None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit19 ansatz.

        Length of flattened vector must be n_qubits*3
        because for >1 qubits there are three gates

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CRX(
                    w[w_idx],
                    wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                    **kwargs,
                )
                w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit19 ansatz.

Length of flattened vector must be n_qubits*3 because for >1 qubits there are three gates

Parameters

w : np.ndarray Weight vector of size n_qubits*3 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit19 ansatz.

    Length of flattened vector must be n_qubits*3
    because for >1 qubits there are three gates

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CRX(
                w[w_idx],
                wires=[n_qubits - q - 1, (n_qubits - q) % n_qubits],
                **kwargs,
            )
            w_idx += 1

get_control_indices(n_qubits) staticmethod

Returns the indices for the controlled rotation gates for one layer. Indices should slice the list of all parameters for one layer as follows: [indices[0]:indices[1]:indices[2]]

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    Returns the indices for the controlled rotation gates for one layer.
    Indices should slice the list of all parameters for one layer as follows:
    [indices[0]:indices[1]:indices[2]]

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-n_qubits, None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_19.

The number of parameters is 3 times the number of qubits when there is more than one qubit, as each qubit contributes 3 parameters. If the number of qubits is less than 2, a warning is logged since no entanglement is possible, and a fixed number of 2 parameters is used.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters required for one layer of the circuit

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_19.

    The number of parameters is 3 times the number of qubits when there
    is more than one qubit, as each qubit contributes 3 parameters.
    If the number of qubits is less than 2, a warning is logged since
    no entanglement is possible, and a fixed number of 2 parameters is used.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters required for one layer of the circuit
    """
    if n_qubits > 1:
        return n_qubits * 3
    else:
        log.warning("Number of Qubits < 2, no entanglement available")
        return 2

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_19.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRX) on each qubit if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_19.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRX`) on each qubit if more than one
    qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    if n_qubits > 1:
        n_params += PulseInformation.num_params("CRX") * n_qubits

    return n_params

Circuit_2

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_2(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_2.

        The total number of parameters is determined by the number of qubits, with
        each qubit contributing 2 parameters.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 2

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_2.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CX`) on each qubit except one if more
        than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        if n_qubits > 1:
            n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

        return 0

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit2 ansatz.

        Length of flattened vector must be n_qubits*2

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*2
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        for q in range(n_qubits - 1):
            Gates.CX(
                wires=[n_qubits - q - 1, n_qubits - q - 2],
                **kwargs,
            )

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit2 ansatz.

Length of flattened vector must be n_qubits*2

Parameters

w : np.ndarray Weight vector of size n_qubits*2 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit2 ansatz.

    Length of flattened vector must be n_qubits*2

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*2
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    for q in range(n_qubits - 1):
        Gates.CX(
            wires=[n_qubits - q - 1, n_qubits - q - 2],
            **kwargs,
        )

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_2.

The total number of parameters is determined by the number of qubits, with each qubit contributing 2 parameters.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_2.

    The total number of parameters is determined by the number of qubits, with
    each qubit contributing 2 parameters.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 2

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_2.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CX) on each qubit except one if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_2.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CX`) on each qubit except one if more
    than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    if n_qubits > 1:
        n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

    return 0

Circuit_3

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_3(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Calculates the number of parameters per layer for Circuit3.

        The number of parameters per layer is given by the number of qubits, with
        each qubit contributing 3 parameters. The last qubit only contributes 2
        parameters because it is the target qubit for the controlled gates.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 3 - 1

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_3.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRZ`) on each qubit except one if more
        than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params = PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_params += (n_qubits - 1) * PulseInformation.num_params("CRZ")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-(n_qubits - 1), None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit3 ansatz.

        Length of flattened vector must be n_qubits*3-1

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3-1
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        for q in range(n_qubits - 1):
            Gates.CRZ(
                w[w_idx],
                wires=[n_qubits - q - 1, n_qubits - q - 2],
                **kwargs,
            )
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit3 ansatz.

Length of flattened vector must be n_qubits*3-1

Parameters

w : np.ndarray Weight vector of size n_qubits*3-1 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit3 ansatz.

    Length of flattened vector must be n_qubits*3-1

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3-1
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    for q in range(n_qubits - 1):
        Gates.CRZ(
            w[w_idx],
            wires=[n_qubits - q - 1, n_qubits - q - 2],
            **kwargs,
        )
        w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-(n_qubits - 1), None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Calculates the number of parameters per layer for Circuit3.

The number of parameters per layer is given by the number of qubits, with each qubit contributing 3 parameters. The last qubit only contributes 2 parameters because it is the target qubit for the controlled gates.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Calculates the number of parameters per layer for Circuit3.

    The number of parameters per layer is given by the number of qubits, with
    each qubit contributing 3 parameters. The last qubit only contributes 2
    parameters because it is the target qubit for the controlled gates.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 3 - 1

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_3.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRZ) on each qubit except one if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_3.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRZ`) on each qubit except one if more
    than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params = PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_params += (n_qubits - 1) * PulseInformation.num_params("CRZ")

    return n_params

Circuit_4

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_4(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the Circuit_4 ansatz.

        The number of parameters is calculated as n_qubits*3-1.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 3 - 1

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_4.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRX`) on each qubit except one if more
        than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("RX")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_params += (n_qubits - 1) * PulseInformation.num_params("CRX")

        return 0

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        if n_qubits > 1:
            return [-(n_qubits - 1), None, None]
        else:
            return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit4 ansatz.

        Length of flattened vector must be n_qubits*3-1

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3-1
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        for q in range(n_qubits - 1):
            Gates.CRX(
                w[w_idx],
                wires=[n_qubits - q - 1, n_qubits - q - 2],
                **kwargs,
            )
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit4 ansatz.

Length of flattened vector must be n_qubits*3-1

Parameters

w : np.ndarray Weight vector of size n_qubits*3-1 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit4 ansatz.

    Length of flattened vector must be n_qubits*3-1

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3-1
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    for q in range(n_qubits - 1):
        Gates.CRX(
            w[w_idx],
            wires=[n_qubits - q - 1, n_qubits - q - 2],
            **kwargs,
        )
        w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    if n_qubits > 1:
        return [-(n_qubits - 1), None, None]
    else:
        return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Circuit_4 ansatz.

The number of parameters is calculated as n_qubits*3-1.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the Circuit_4 ansatz.

    The number of parameters is calculated as n_qubits*3-1.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 3 - 1

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_4.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRX) on each qubit except one if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_4.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRX`) on each qubit except one if more
    than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("RX")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_params += (n_qubits - 1) * PulseInformation.num_params("CRX")

    return 0

Circuit_6

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_6(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_6.

        The total number of parameters is n_qubits*3+n_qubits**2, which is
        the number of rotations n_qubits*3 plus the number of entangling gates
        n_qubits**2.

        If n_qubits is 1, the number of parameters is 4, and a warning is logged
        since no entanglement is possible.

        Parameters
        ----------
        n_qubits : int
            Number of qubits

        Returns
        -------
        int
            Number of parameters per layer
        """
        if n_qubits > 1:
            return n_qubits * 3 + n_qubits**2
        else:
            log.warning("Number of Qubits < 2, no entanglement available")
            return 4

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_6.

        This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
        qubits, and controlled rotations (`CRX`) on each qubit twice except repeats
        if more than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = 2 * PulseInformation.num_params("RX")
        n_params += 2 * PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_CRX = n_qubits * (n_qubits - 1)
        n_params += n_CRX * PulseInformation.num_params("CRX")

        return 0

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        Returns the indices for the controlled rotation gates for one layer.
        Indices should slice the list of all parameters for one layer as follows:
        [indices[0]:indices[1]:indices[2]]

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        # TODO: implement
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit6 ansatz.

        Length of flattened vector must be
            n_qubits*4+n_qubits*(n_qubits-1) =
            n_qubits*3+n_qubits**2

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size
                n_layers*(n_qubits*3+n_qubits**2)
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for ql in range(n_qubits):
                for q in range(n_qubits):
                    if q == ql:
                        continue
                    Gates.CRX(
                        w[w_idx],
                        wires=[n_qubits - ql - 1, (n_qubits - q - 1) % n_qubits],
                        **kwargs,
                    )
                    w_idx += 1

        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit6 ansatz.

Length of flattened vector must be n_qubits4+n_qubits(n_qubits-1) = n_qubits3+n_qubits*2

Parameters

w : np.ndarray Weight vector of size n_layers(n_qubits3+n_qubits**2) n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit6 ansatz.

    Length of flattened vector must be
        n_qubits*4+n_qubits*(n_qubits-1) =
        n_qubits*3+n_qubits**2

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size
            n_layers*(n_qubits*3+n_qubits**2)
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for ql in range(n_qubits):
            for q in range(n_qubits):
                if q == ql:
                    continue
                Gates.CRX(
                    w[w_idx],
                    wires=[n_qubits - ql - 1, (n_qubits - q - 1) % n_qubits],
                    **kwargs,
                )
                w_idx += 1

    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1

get_control_indices(n_qubits) staticmethod

Returns the indices for the controlled rotation gates for one layer. Indices should slice the list of all parameters for one layer as follows: [indices[0]:indices[1]:indices[2]]

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    Returns the indices for the controlled rotation gates for one layer.
    Indices should slice the list of all parameters for one layer as follows:
    [indices[0]:indices[1]:indices[2]]

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    # TODO: implement
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_6.

The total number of parameters is n_qubits3+n_qubits2, which is the number of rotations n_qubits3 plus the number of entangling gates n_qubits**2.

If n_qubits is 1, the number of parameters is 4, and a warning is logged since no entanglement is possible.

Parameters

n_qubits : int Number of qubits

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_6.

    The total number of parameters is n_qubits*3+n_qubits**2, which is
    the number of rotations n_qubits*3 plus the number of entangling gates
    n_qubits**2.

    If n_qubits is 1, the number of parameters is 4, and a warning is logged
    since no entanglement is possible.

    Parameters
    ----------
    n_qubits : int
        Number of qubits

    Returns
    -------
    int
        Number of parameters per layer
    """
    if n_qubits > 1:
        return n_qubits * 3 + n_qubits**2
    else:
        log.warning("Number of Qubits < 2, no entanglement available")
        return 4

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_6.

This includes contributions from single-qubit rotations (RX, RZ) on all qubits, and controlled rotations (CRX) on each qubit twice except repeats if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_6.

    This includes contributions from single-qubit rotations (`RX`, `RZ`) on all
    qubits, and controlled rotations (`CRX`) on each qubit twice except repeats
    if more than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = 2 * PulseInformation.num_params("RX")
    n_params += 2 * PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_CRX = n_qubits * (n_qubits - 1)
    n_params += n_CRX * PulseInformation.num_params("CRX")

    return 0

Circuit_9

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Circuit_9(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for Circuit_9.

        The number of parameters is equal to the number of qubits.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters required for one layer of the circuit
        """
        return n_qubits

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Circuit_9.

        This includes contributions from single-qubit rotations (`H`, `RX`) on all
        qubits, and controlled rotations (`CZ`) on each qubit except one if more
        than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("H")
        n_params += PulseInformation.num_params("RX")
        n_params *= n_qubits

        n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Circuit9 ansatz.

        Length of flattened vector must be n_qubits

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.H(wires=q, **kwargs)

        for q in range(n_qubits - 1):
            Gates.CZ(
                wires=[n_qubits - q - 2, n_qubits - q - 1],
                **kwargs,
            )

        for q in range(n_qubits):
            Gates.RX(w[w_idx], wires=q, **kwargs)
            w_idx += 1

build(w, n_qubits, **kwargs) staticmethod

Creates a Circuit9 ansatz.

Length of flattened vector must be n_qubits

Parameters

w : np.ndarray Weight vector of size n_qubits n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Circuit9 ansatz.

    Length of flattened vector must be n_qubits

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.H(wires=q, **kwargs)

    for q in range(n_qubits - 1):
        Gates.CZ(
            wires=[n_qubits - q - 2, n_qubits - q - 1],
            **kwargs,
        )

    for q in range(n_qubits):
        Gates.RX(w[w_idx], wires=q, **kwargs)
        w_idx += 1

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for Circuit_9.

The number of parameters is equal to the number of qubits.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters required for one layer of the circuit

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for Circuit_9.

    The number of parameters is equal to the number of qubits.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters required for one layer of the circuit
    """
    return n_qubits

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Circuit_9.

This includes contributions from single-qubit rotations (H, RX) on all qubits, and controlled rotations (CZ) on each qubit except one if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Circuit_9.

    This includes contributions from single-qubit rotations (`H`, `RX`) on all
    qubits, and controlled rotations (`CZ`) on each qubit except one if more
    than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("H")
    n_params += PulseInformation.num_params("RX")
    n_params *= n_qubits

    n_params += (n_qubits - 1) * PulseInformation.num_params("CZ")

    return n_params

GHZ

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class GHZ(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        return 0

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for the GHZ circuit.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Total number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("H")
        n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        Gates.H(0, **kwargs)

        for q in range(n_qubits - 1):
            Gates.CX([q, q + 1], **kwargs)

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for the GHZ circuit.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Total number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for the GHZ circuit.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Total number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("H")
    n_params += (n_qubits - 1) * PulseInformation.num_params("CX")

    return n_params

Hardware_Efficient

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Hardware_Efficient(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the
        Hardware Efficient Ansatz.

        The number of parameters is 3 times the number of qubits when there
        is more than one qubit, as each qubit contributes 3 parameters.
        If the number of qubits is less than 2, a warning is logged since
        no entanglement is possible, and a fixed number of 2 parameters is used.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of parameters required for one layer of the circuit.
        """
        if n_qubits < 2:
            log.warning("Number of Qubits < 2, no entanglement available")
        return n_qubits * 3

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for the
        Hardware Efficient Ansatz.

        This counts all parameters needed if the circuit is used at the
        pulse level. It includes contributions from single-qubit rotations
        (`RY` and `RZ`) and multi-qubit gates (`CX`) if more than one qubit
        is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = 2 * PulseInformation.num_params("RY")
        n_params += PulseInformation.num_params("RZ")
        n_params *= n_qubits

        n_CX = (n_qubits // 2) + ((n_qubits - 1) // 2)
        n_CX += 1 if n_qubits > 2 else 0
        n_params += n_CX * PulseInformation.num_params("CX")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a Hardware-Efficient ansatz, as proposed in
        https://arxiv.org/pdf/2309.03279

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, **kwargs)
            w_idx += 1
            Gates.RY(w[w_idx], wires=q, **kwargs)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits // 2):
                Gates.CX(wires=[(2 * q), (2 * q + 1)], **kwargs)
            for q in range((n_qubits - 1) // 2):
                Gates.CX(wires=[(2 * q + 1), (2 * q + 2)], **kwargs)
            if n_qubits > 2:
                Gates.CX(wires=[(n_qubits - 1), 0], **kwargs)

build(w, n_qubits, **kwargs) staticmethod

Creates a Hardware-Efficient ansatz, as proposed in https://arxiv.org/pdf/2309.03279

Parameters

w : np.ndarray Weight vector of size n_qubits*3 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a Hardware-Efficient ansatz, as proposed in
    https://arxiv.org/pdf/2309.03279

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RZ(w[w_idx], wires=q, **kwargs)
        w_idx += 1
        Gates.RY(w[w_idx], wires=q, **kwargs)
        w_idx += 1

    if n_qubits > 1:
        for q in range(n_qubits // 2):
            Gates.CX(wires=[(2 * q), (2 * q + 1)], **kwargs)
        for q in range((n_qubits - 1) // 2):
            Gates.CX(wires=[(2 * q + 1), (2 * q + 2)], **kwargs)
        if n_qubits > 2:
            Gates.CX(wires=[(n_qubits - 1), 0], **kwargs)

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Hardware Efficient Ansatz.

The number of parameters is 3 times the number of qubits when there is more than one qubit, as each qubit contributes 3 parameters. If the number of qubits is less than 2, a warning is logged since no entanglement is possible, and a fixed number of 2 parameters is used.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the
    Hardware Efficient Ansatz.

    The number of parameters is 3 times the number of qubits when there
    is more than one qubit, as each qubit contributes 3 parameters.
    If the number of qubits is less than 2, a warning is logged since
    no entanglement is possible, and a fixed number of 2 parameters is used.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of parameters required for one layer of the circuit.
    """
    if n_qubits < 2:
        log.warning("Number of Qubits < 2, no entanglement available")
    return n_qubits * 3

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for the Hardware Efficient Ansatz.

This counts all parameters needed if the circuit is used at the pulse level. It includes contributions from single-qubit rotations (RY and RZ) and multi-qubit gates (CX) if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for the
    Hardware Efficient Ansatz.

    This counts all parameters needed if the circuit is used at the
    pulse level. It includes contributions from single-qubit rotations
    (`RY` and `RZ`) and multi-qubit gates (`CX`) if more than one qubit
    is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = 2 * PulseInformation.num_params("RY")
    n_params += PulseInformation.num_params("RZ")
    n_params *= n_qubits

    n_CX = (n_qubits // 2) + ((n_qubits - 1) // 2)
    n_CX += 1 if n_qubits > 2 else 0
    n_params += n_CX * PulseInformation.num_params("CX")

    return n_params

No_Entangling

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class No_Entangling(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the NoEntangling ansatz.

        The number of parameters is calculated as n_qubits*3.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        return n_qubits * 3

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for No_Entangling circuit.

        This includes contributions from single-qubit rotations (`Rot`) on all
        qubits only.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = PulseInformation.num_params("Rot")
        n_params *= n_qubits

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs):
        """
        Creates a circuit without entangling, but with U3 gates on all qubits

        Length of flattened vector must be n_qubits*3

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*3
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.Rot(
                w[w_idx],
                w[w_idx + 1],
                w[w_idx + 2],
                wires=q,
                **kwargs,
            )
            w_idx += 3

build(w, n_qubits, **kwargs) staticmethod

Creates a circuit without entangling, but with U3 gates on all qubits

Length of flattened vector must be n_qubits*3

Parameters

w : np.ndarray Weight vector of size n_qubits*3 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs):
    """
    Creates a circuit without entangling, but with U3 gates on all qubits

    Length of flattened vector must be n_qubits*3

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*3
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.Rot(
            w[w_idx],
            w[w_idx + 1],
            w[w_idx + 2],
            wires=q,
            **kwargs,
        )
        w_idx += 3

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the NoEntangling ansatz.

The number of parameters is calculated as n_qubits*3.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the NoEntangling ansatz.

    The number of parameters is calculated as n_qubits*3.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    return n_qubits * 3

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for No_Entangling circuit.

This includes contributions from single-qubit rotations (Rot) on all qubits only.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for No_Entangling circuit.

    This includes contributions from single-qubit rotations (`Rot`) on all
    qubits only.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = PulseInformation.num_params("Rot")
    n_params *= n_qubits

    return n_params

Strongly_Entangling

Bases: Circuit

Source code in qml_essentials/ansaetze.py

class Strongly_Entangling(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of parameters per layer for the
        Strongly Entangling ansatz.

        The number of parameters is calculated as n_qubits*6.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        int
            Number of parameters per layer
        """
        if n_qubits < 2:
            log.warning("Number of Qubits < 2, no entanglement available")
        return n_qubits * 6

    @staticmethod
    def n_pulse_params_per_layer(n_qubits: int) -> int:
        """
        Returns the number of pulse parameters per layer for Strongly_Entangling
        circuit.

        This includes contributions from single-qubit rotations (`Rot`) on all
        qubits, and controlled rotations (`CX`) if more than one qubit is present.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit.

        Returns
        -------
        int
            Number of pulse parameters required for one layer of the circuit.
        """
        n_params = 2 * PulseInformation.num_params("Rot")
        n_params *= n_qubits

        if n_qubits > 1:
            n_params += n_qubits * 2 * PulseInformation.num_params("CX")

        return n_params

    @staticmethod
    def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
        """
        No controlled rotation gates available. Always None.

        Parameters
        ----------
        n_qubits : int
            Number of qubits in the circuit

        Returns
        -------
        Optional[np.ndarray]
            List of all controlled indices, or None if the circuit does not
            contain controlled rotation gates.
        """
        return None

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, **kwargs) -> None:
        """
        Creates a Strongly Entangling ansatz.

        Length of flattened vector must be n_qubits*6

        Parameters
        ----------
        w : np.ndarray
            Weight vector of size n_qubits*6
        n_qubits : int
            Number of qubits
        noise_params : Optional[Dict[str, float]], optional
            Dictionary of noise parameters to apply to the gates
        """
        w_idx = 0
        for q in range(n_qubits):
            Gates.Rot(
                w[w_idx],
                w[w_idx + 1],
                w[w_idx + 2],
                wires=q,
                **kwargs,
            )
            w_idx += 3

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CX(wires=[q, (q + 1) % n_qubits], **kwargs)

        for q in range(n_qubits):
            Gates.Rot(
                w[w_idx],
                w[w_idx + 1],
                w[w_idx + 2],
                wires=q,
                **kwargs,
            )
            w_idx += 3

        if n_qubits > 1:
            for q in range(n_qubits):
                Gates.CX(
                    wires=[q, (q + n_qubits // 2) % n_qubits],
                    **kwargs,
                )

build(w, n_qubits, **kwargs) staticmethod

Creates a Strongly Entangling ansatz.

Length of flattened vector must be n_qubits*6

Parameters

w : np.ndarray Weight vector of size n_qubits*6 n_qubits : int Number of qubits noise_params : Optional[Dict[str, float]], optional Dictionary of noise parameters to apply to the gates

Source code in qml_essentials/ansaetze.py

@staticmethod
def build(w: np.ndarray, n_qubits: int, **kwargs) -> None:
    """
    Creates a Strongly Entangling ansatz.

    Length of flattened vector must be n_qubits*6

    Parameters
    ----------
    w : np.ndarray
        Weight vector of size n_qubits*6
    n_qubits : int
        Number of qubits
    noise_params : Optional[Dict[str, float]], optional
        Dictionary of noise parameters to apply to the gates
    """
    w_idx = 0
    for q in range(n_qubits):
        Gates.Rot(
            w[w_idx],
            w[w_idx + 1],
            w[w_idx + 2],
            wires=q,
            **kwargs,
        )
        w_idx += 3

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CX(wires=[q, (q + 1) % n_qubits], **kwargs)

    for q in range(n_qubits):
        Gates.Rot(
            w[w_idx],
            w[w_idx + 1],
            w[w_idx + 2],
            wires=q,
            **kwargs,
        )
        w_idx += 3

    if n_qubits > 1:
        for q in range(n_qubits):
            Gates.CX(
                wires=[q, (q + n_qubits // 2) % n_qubits],
                **kwargs,
            )

get_control_indices(n_qubits) staticmethod

No controlled rotation gates available. Always None.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

Optional[np.ndarray] List of all controlled indices, or None if the circuit does not contain controlled rotation gates.

Source code in qml_essentials/ansaetze.py

@staticmethod
def get_control_indices(n_qubits: int) -> Optional[np.ndarray]:
    """
    No controlled rotation gates available. Always None.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    Optional[np.ndarray]
        List of all controlled indices, or None if the circuit does not
        contain controlled rotation gates.
    """
    return None

n_params_per_layer(n_qubits) staticmethod

Returns the number of parameters per layer for the Strongly Entangling ansatz.

The number of parameters is calculated as n_qubits*6.

Parameters

n_qubits : int Number of qubits in the circuit

Returns

int Number of parameters per layer

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of parameters per layer for the
    Strongly Entangling ansatz.

    The number of parameters is calculated as n_qubits*6.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit

    Returns
    -------
    int
        Number of parameters per layer
    """
    if n_qubits < 2:
        log.warning("Number of Qubits < 2, no entanglement available")
    return n_qubits * 6

n_pulse_params_per_layer(n_qubits) staticmethod

Returns the number of pulse parameters per layer for Strongly_Entangling circuit.

This includes contributions from single-qubit rotations (Rot) on all qubits, and controlled rotations (CX) if more than one qubit is present.

Parameters

n_qubits : int Number of qubits in the circuit.

Returns

int Number of pulse parameters required for one layer of the circuit.

Source code in qml_essentials/ansaetze.py

@staticmethod
def n_pulse_params_per_layer(n_qubits: int) -> int:
    """
    Returns the number of pulse parameters per layer for Strongly_Entangling
    circuit.

    This includes contributions from single-qubit rotations (`Rot`) on all
    qubits, and controlled rotations (`CX`) if more than one qubit is present.

    Parameters
    ----------
    n_qubits : int
        Number of qubits in the circuit.

    Returns
    -------
    int
        Number of pulse parameters required for one layer of the circuit.
    """
    n_params = 2 * PulseInformation.num_params("Rot")
    n_params *= n_qubits

    if n_qubits > 1:
        n_params += n_qubits * 2 * PulseInformation.num_params("CX")

    return n_params

Gates

from qml_essentials.ansaetze import Gates

Dynamic accessor for quantum gates.

Routes calls like Gates.RX(...) to either UnitaryGates or PulseGates depending on the gate_mode keyword (defaults to 'unitary').

During circuit building, the pulse manager can be activated via pulse_manager_context, which slices the global model pulse parameters and passes them to each gate. Model pulse parameters act as element-wise scalers on the gate's optimized pulse parameters.

Parameters

gate_mode : str, optional Determines the backend. 'unitary' for UnitaryGates, 'pulse' for PulseGates. Defaults to 'unitary'.

Examples

Gates.RX(w, wires) Gates.RX(w, wires, gate_mode="unitary") Gates.RX(w, wires, gate_mode="pulse") Gates.RX(w, wires, pulse_params, gate_mode="pulse")

Source code in qml_essentials/ansaetze.py

class Gates(metaclass=GatesMeta):
    """
    Dynamic accessor for quantum gates.

    Routes calls like `Gates.RX(...)` to either `UnitaryGates` or `PulseGates`
    depending on the `gate_mode` keyword (defaults to 'unitary').

    During circuit building, the pulse manager can be activated via
    `pulse_manager_context`, which slices the global model pulse parameters
    and passes them to each gate. Model pulse parameters act as element-wise
    scalers on the gate's optimized pulse parameters.

    Parameters
    ----------
    gate_mode : str, optional
        Determines the backend. 'unitary' for UnitaryGates, 'pulse' for PulseGates.
        Defaults to 'unitary'.

    Examples
    --------
    >>> Gates.RX(w, wires)
    >>> Gates.RX(w, wires, gate_mode="unitary")
    >>> Gates.RX(w, wires, gate_mode="pulse")
    >>> Gates.RX(w, wires, pulse_params, gate_mode="pulse")
    """

    def __getattr__(self, gate_name):
        def handler(**kwargs):
            return self._inner_getattr(gate_name, **kwargs)

        return handler

    @staticmethod
    def _inner_getattr(gate_name, *args, **kwargs):
        gate_mode = kwargs.pop("gate_mode", "unitary")

        # Backend selection and kwargs filtering
        allowed_args = ["w", "wires", "phi", "theta", "omega"]
        if gate_mode == "unitary":
            gate_backend = UnitaryGates
            allowed_args += ["noise_params"]
        elif gate_mode == "pulse":
            gate_backend = PulseGates
            allowed_args += ["pulse_params"]
        else:
            raise ValueError(
                f"Unknown gate mode: {gate_mode}. Use 'unitary' or 'pulse'."
            )

        kwargs = {k: v for k, v in kwargs.items() if k in allowed_args}
        pulse_params = kwargs.get("pulse_params")
        pulse_mgr = getattr(Gates, "_pulse_mgr", None)

        # Type check on pulse parameters
        if pulse_params is not None:
            # flatten pulse parameters
            if isinstance(pulse_params, (list, tuple)):
                flat_params = pulse_params

            elif isinstance(pulse_params, jax.core.Tracer):
                flat_params = jnp.ravel(pulse_params)

            elif isinstance(pulse_params, (np.ndarray, jnp.ndarray)):
                flat_params = pulse_params.flatten().tolist()

            else:
                raise TypeError(f"Unsupported pulse_params type: {type(pulse_params)}")

            # checks elements in flat parameters are real numbers or jax Tracer
            if not all(
                isinstance(x, (numbers.Real, jax.core.Tracer)) for x in flat_params
            ):
                raise TypeError(
                    "All elements in pulse_params must be int or float, "
                    f"got {pulse_params}, type {type(pulse_params)}. "
                )

        # Len check on pulse parameters
        if pulse_params is not None and not isinstance(pulse_mgr, PulseParamManager):
            n_params = PulseInformation.num_params(gate_name)
            if len(flat_params) != n_params:
                raise ValueError(
                    f"Gate '{gate_name}' expects {n_params} pulse parameters, "
                    f"got {len(flat_params)}"
                )

        # Pulse slicing + scaling
        if gate_mode == "pulse" and isinstance(pulse_mgr, PulseParamManager):
            n_params = PulseInformation.num_params(gate_name)
            scalers = pulse_mgr.get(n_params)
            base = PulseInformation.optimized_params(gate_name)
            kwargs["pulse_params"] = scalers * base  # element-wise scaling

        # Call the selected gate backend
        gate = getattr(gate_backend, gate_name, None)
        if gate is None:
            raise AttributeError(
                f"'{gate_backend.__class__.__name__}' object "
                f"has no attribute '{gate_name}'"
            )

        return gate(*args, **kwargs)

    @staticmethod
    @contextmanager
    def pulse_manager_context(pulse_params: np.ndarray):
        """Temporarily set the global pulse manager for circuit building."""
        Gates._pulse_mgr = PulseParamManager(pulse_params)
        try:
            yield
        finally:
            Gates._pulse_mgr = None

pulse_manager_context(pulse_params) staticmethod

Temporarily set the global pulse manager for circuit building.

Source code in qml_essentials/ansaetze.py

@staticmethod
@contextmanager
def pulse_manager_context(pulse_params: np.ndarray):
    """Temporarily set the global pulse manager for circuit building."""
    Gates._pulse_mgr = PulseParamManager(pulse_params)
    try:
        yield
    finally:
        Gates._pulse_mgr = None

Model

from qml_essentials.model import Model

A quantum circuit model.

Source code in qml_essentials/model.py

class Model:
    """
    A quantum circuit model.
    """

    lightning_threshold = 12
    cpu_scaler = 0.9  # default cpu scaler, =1 means full CPU for MP

    def __init__(
        self,
        n_qubits: int,
        n_layers: int,
        circuit_type: Union[str, Circuit] = "No_Ansatz",
        data_reupload: Union[bool, List[bool], List[List[bool]]] = True,
        state_preparation: Union[str, Callable, List[str], List[Callable]] = None,
        encoding: Union[str, Callable, List[str], List[Callable]] = Gates.RX,
        trainable_frequencies: bool = False,
        initialization: str = "random",
        initialization_domain: List[float] = [0, 2 * np.pi],
        output_qubit: Union[List[int], int] = -1,
        shots: Optional[int] = None,
        random_seed: int = 1000,
        as_pauli_circuit: bool = False,
        remove_zero_encoding: bool = True,
        mp_threshold: int = -1,
    ) -> None:
        """
        Initialize the quantum circuit model.
        Parameters will have the shape [impl_n_layers, parameters_per_layer]
        where impl_n_layers is the number of layers provided and added by one
        depending if data_reupload is True and parameters_per_layer is given by
        the chosen ansatz.

        The model is initialized with the following parameters as defaults:
        - noise_params: None
        - execution_type: "expval"
        - shots: None

        Args:
            n_qubits (int): The number of qubits in the circuit.
            n_layers (int): The number of layers in the circuit.
            circuit_type (str, Circuit): The type of quantum circuit to use.
                If None, defaults to "no_ansatz".
            data_reupload (Union[bool, List[bool], List[List[bool]]], optional):
                Whether to reupload data to the quantum device on each
                layer and qubit. Detailed re-uploading instructions can be given
                as a list/array of 0/False and 1/True with shape (n_qubits,
                n_layers) to specify where to upload the data. Defaults to True
                for applying data re-uploading to the full circuit.
            encoding (Union[str, Callable, List[str], List[Callable]], optional):
                The unitary to use for encoding the input data. Can be a string
                (e.g. "RX") or a callable (e.g. qml.RX). Defaults to qml.RX.
                If input is multidimensional it is assumed to be a list of
                unitaries or a list of strings.
            trainable_frequencies (bool, optional):
                Sets trainable encoding parameters for trainable frequencies.
                Defaults to False.
            initialization (str, optional): The strategy to initialize the parameters.
                Can be "random", "zeros", "zero-controlled", "pi", or "pi-controlled".
                Defaults to "random".
            output_qubit (List[int], int, optional): The index of the output
                qubit (or qubits). When set to -1 all qubits are measured, or a
                global measurement is conducted, depending on the execution
                type.
            shots (Optional[int], optional): The number of shots to use for
                the quantum device. Defaults to None.
            random_seed (int, optional): seed for the random number generator
                in initialization is "random" and for random noise parameters.
                Defaults to 1000.
            as_pauli_circuit (bool, optional): whether the circuit is
                transformed to a Pauli-Clifford circuit as described by Nemkov
                et al. (https://doi.org/10.1103/PhysRevA.108.032406), which is
                required for analytical Fourier coefficient computation.
                Defaults to False.
            remove_zero_encoding (bool, optional): whether to
                remove the zero encoding from the circuit. Defaults to True.
            mp_threshold (int, optional): threshold above which the parameter
                batch dimension is split across multiple processes.
                Defaults to -1.

        Returns:
            None
        """
        # Initialize default parameters needed for circuit evaluation
        self.noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None
        self.execution_type: Optional[str] = "expval"
        self.shots = shots
        self.remove_zero_encoding = remove_zero_encoding
        self.mp_threshold = mp_threshold
        self.n_qubits: int = n_qubits
        self.n_layers: int = n_layers
        self.trainable_frequencies: bool = trainable_frequencies

        if isinstance(output_qubit, list):
            assert (
                len(output_qubit) <= n_qubits
            ), f"Size of output_qubit {len(output_qubit)} cannot be\
            larger than number of qubits {n_qubits}."
        self.output_qubit: Union[List[int], int] = output_qubit

        # Initialize rng in Gates
        Gates.init_rng(random_seed)

        # --- State Preparation ---
        # first check if we have a str, list or callable
        if isinstance(state_preparation, str):
            # if str, use the pennylane fct
            self._sp = [getattr(Gates, f"{state_preparation}")]
        elif isinstance(state_preparation, list):
            # if list, check if str or callable
            if isinstance(state_preparation[0], str):
                self._sp = [getattr(Gates, f"{sp}") for sp in state_preparation]
            else:
                self._sp = state_preparation
        elif state_preparation is None:
            self._sp = [lambda *args, **kwargs: None]
        else:
            # default to callable
            self._sp = [state_preparation]

        # prepare corresponding pulse parameters (always optimized pulses)
        self.sp_pulse_params = []
        for sp in self._sp:
            sp_name = sp.__name__ if hasattr(sp, "__name__") else str(sp)

            if sp_name in pinfo.OPTIMIZED_PULSES:
                params = np.array(pinfo.optimized_params(sp_name), requires_grad=False)
                self.sp_pulse_params.append(params)
            else:
                # gate has no pulse parametrization
                self.sp_pulse_params.append(None)

        # --- Encoding ---
        # first check if we have a str, list or callable
        if isinstance(encoding, str):
            # if str, use the pennylane fct
            self._enc = [getattr(Gates, f"{encoding}")]
        elif isinstance(encoding, list):
            # if list, check if str or callable
            if isinstance(encoding[0], str):
                self._enc = [getattr(Gates, f"{enc}") for enc in encoding]
            else:
                self._enc = encoding
        else:
            # default to callable
            self._enc = [encoding]

        # Number of possible inputs
        self.n_input_feat = len(self._enc)
        log.info(f"Number of input features: {self.n_input_feat}")

        # Trainable frequencies, default initialization as in arXiv:2309.03279v2
        self.enc_params = np.ones(
            (self.n_qubits, self.n_input_feat), requires_grad=trainable_frequencies
        )

        # --- Data-Reuploading ---
        # Process data reuploading strategy and set degree
        if not isinstance(data_reupload, bool):
            if not isinstance(data_reupload, np.ndarray):
                data_reupload = np.array(data_reupload)
            if data_reupload.shape == (
                n_layers,
                n_qubits,
            ):
                data_reupload = data_reupload.reshape(*data_reupload.shape, 1)
                data_reupload = np.repeat(data_reupload, self.n_input_feat, axis=2)

            assert data_reupload.shape == (
                n_layers,
                n_qubits,
                self.n_input_feat,
            ), f"Data reuploading array has wrong shape. \
                Expected {(n_layers, n_qubits)} or\
                {(n_layers, n_qubits, self.n_input_feat)},\
                got {data_reupload.shape}."

            log.debug(f"Data reuploading array:\n{data_reupload}")
        else:
            if data_reupload:
                impl_n_layers: int = (
                    n_layers + 1
                )  # we need L+1 according to Schuld et al.
                data_reupload = np.ones((n_layers, n_qubits, self.n_input_feat))
                log.debug("Full data reuploading.")
            else:
                impl_n_layers: int = n_layers
                data_reupload = np.zeros((n_layers, n_qubits, self.n_input_feat))
                data_reupload[0][0] = 1
                log.debug("No data reuploading.")

        # convert to boolean values
        self.data_reupload = data_reupload.astype(bool)
        self.frequencies = [
            np.count_nonzero(self.data_reupload[..., i])
            for i in range(self.n_input_feat)
        ]

        if self.degree > 1:
            impl_n_layers: int = n_layers + 1  # we need L+1 according to Schuld et al.
        else:
            impl_n_layers = n_layers
        log.info(f"Number of implicit layers: {impl_n_layers}.")

        # --- Ansatz ---
        # only weak check for str. We trust the user to provide sth useful
        if isinstance(circuit_type, str):
            self.pqc: Callable[[Optional[np.ndarray], int], int] = getattr(
                Ansaetze, circuit_type or "No_Ansatz"
            )()
        else:
            self.pqc = circuit_type()
        log.info(f"Using Ansatz {circuit_type}.")

        # calculate the shape of the parameter vector here, we will re-use this in init.
        params_per_layer = self.pqc.n_params_per_layer(self.n_qubits)
        self._params_shape: Tuple[int, int] = (impl_n_layers, params_per_layer)
        log.info(f"Parameters per layer: {params_per_layer}")

        pulse_params_per_layer = self.pqc.n_pulse_params_per_layer(self.n_qubits)
        self._pulse_params_shape: Tuple[int, int] = (
            impl_n_layers,
            pulse_params_per_layer,
        )

        self.batch_shape = (1, 1)
        # this will also be re-used in the init method,
        # however, only if nothing is provided
        self._inialization_strategy = initialization
        self._initialization_domain = initialization_domain

        # ..here! where we only require a rng
        self.initialize_params(np.random.default_rng(random_seed))

        # Initialize two circuits, one with the default device and
        # one with the mixed device
        # which allows us to later route depending on the state_vector flag
        self.as_pauli_circuit = as_pauli_circuit

        self.circuit_mixed: qml.QNode = qml.QNode(
            self._circuit,
            qml.device("default.mixed", shots=self.shots, wires=self.n_qubits),
            interface="autograd" if self.shots is not None else "auto",
            diff_method="parameter-shift" if self.shots is not None else "best",
        )

    @property
    def degree(self):
        return max(self.frequencies)

    @property
    def as_pauli_circuit(self) -> bool:
        return self._as_pauli_circuit

    @as_pauli_circuit.setter
    def as_pauli_circuit(self, value: bool) -> None:
        self._as_pauli_circuit = value

        if self.n_qubits < self.lightning_threshold:
            device = "default.qubit"
        else:
            device = "lightning.qubit"
            self.mp_threshold = -1

        self.circuit: qml.QNode = qml.QNode(
            self._circuit,
            qml.device(
                device,
                shots=self.shots,
                wires=self.n_qubits,
            ),
            interface="autograd" if self.shots is not None else "auto",
            diff_method="parameter-shift" if self.shots is not None else "best",
        )

        if value:
            pauli_circuit_transform = qml.transform(
                PauliCircuit.from_parameterised_circuit
            )
            self.circuit = pauli_circuit_transform(self.circuit)

    @property
    def noise_params(self) -> Optional[Dict[str, Union[float, Dict[str, float]]]]:
        """
        Gets the noise parameters of the model.

        Returns:
            Optional[Dict[str, float]]: A dictionary of
            noise parameters or None if not set.
        """
        return self._noise_params

    @noise_params.setter
    def noise_params(
        self, kvs: Optional[Dict[str, Union[float, Dict[str, float]]]]
    ) -> None:
        """
        Sets the noise parameters of the model.

        Typically a "noise parameter" refers to the error probability.
        ThermalRelaxation is a special case, and supports a dict as value with
        structure:
            "ThermalRelaxation":
            {
                "t1": 2000, # relative t1 time.
                "t2": 1000, # relative t2 time
                "t_factor" 1: # relative gate time factor
            },

        Args:
            kvs (Optional[Dict[str, Union[float, Dict[str, float]]]]): A
            dictionary of noise parameters. If all values are 0.0, the noise
            parameters are set to None.

        Returns:
            None
        """
        # set to None if only zero values provided
        if kvs is not None and all(np == 0.0 for np in kvs.values()):
            kvs = None

        # set default values
        if kvs is not None:
            kvs.setdefault("BitFlip", 0.0)
            kvs.setdefault("PhaseFlip", 0.0)
            kvs.setdefault("Depolarizing", 0.0)
            kvs.setdefault("MultiQubitDepolarizing", 0.0)
            kvs.setdefault("AmplitudeDamping", 0.0)
            kvs.setdefault("PhaseDamping", 0.0)
            kvs.setdefault("GateError", 0.0)
            kvs.setdefault("ThermalRelaxation", None)
            kvs.setdefault("StatePreparation", 0.0)
            kvs.setdefault("Measurement", 0.0)

            # check if there are any keys not supported
            for key in kvs.keys():
                if key not in [
                    "BitFlip",
                    "PhaseFlip",
                    "Depolarizing",
                    "MultiQubitDepolarizing",
                    "AmplitudeDamping",
                    "PhaseDamping",
                    "GateError",
                    "ThermalRelaxation",
                    "StatePreparation",
                    "Measurement",
                ]:
                    warnings.warn(
                        f"Noise type {key} is not supported by this package",
                        UserWarning,
                    )

            # check valid params for thermal relaxation noise channel
            tr_params = kvs["ThermalRelaxation"]
            if isinstance(tr_params, dict):
                tr_params.setdefault("t1", 0.0)
                tr_params.setdefault("t2", 0.0)
                tr_params.setdefault("t_factor", 0.0)
                for k in tr_params.keys():
                    if k not in [
                        "t1",
                        "t2",
                        "t_factor",
                    ]:
                        warnings.warn(
                            f"Thermal Relaxation parameter {k} is not supported "
                            f"by this package",
                            UserWarning,
                        )
                if not all(tr_params.values()) or tr_params["t2"] > 2 * tr_params["t1"]:
                    warnings.warn(
                        "Received invalid values for Thermal Relaxation noise "
                        "parameter. Thermal relaxation is not applied!",
                        UserWarning,
                    )
                    kvs["ThermalRelaxation"] = 0.0

        self._noise_params = kvs

    @property
    def execution_type(self) -> str:
        """
        Gets the execution type of the model.

        Returns:
            str: The execution type, one of 'density', 'expval', or 'probs'.
        """
        return self._execution_type

    @execution_type.setter
    def execution_type(self, value: str) -> None:
        if value not in ["density", "state", "expval", "probs"]:
            raise ValueError(f"Invalid execution type: {value}.")

        if (value == "density" or value == "state") and self.output_qubit != -1:
            warnings.warn(
                f"{value} measurement does ignore output_qubit, which is "
                f"{self.output_qubit}.",
                UserWarning,
            )

        if value == "probs" and self.shots is None:
            warnings.warn(
                "Setting execution_type to probs without specifying shots.",
                UserWarning,
            )

        if value == "density" and self.shots is not None:
            warnings.warn(
                "Setting execution_type to density with specified shots.",
                UserWarning,
            )

        self._execution_type = value

    @property
    def shots(self) -> Optional[int]:
        """
        Gets the number of shots to use for the quantum device.

        Returns:
            Optional[int]: The number of shots.
        """
        return self._shots

    @shots.setter
    def shots(self, value: Optional[int]) -> None:
        """
        Sets the number of shots to use for the quantum device.

        Args:
            value (Optional[int]): The number of shots.
            If an integer less than or equal to 0 is provided, it is set to None.

        Returns:
            None
        """
        if type(value) is int and value <= 0:
            value = None
        self._shots = value

    def initialize_params(
        self,
        rng: np.random.Generator,
        repeat: int = None,
        initialization: str = None,
        initialization_domain: List[float] = None,
    ) -> None:
        """
        Initializes the parameters of the model.

        Args:
            rng: A random number generator to use for initialization.
            repeat: The number of times to repeat the parameters.
                If None, the number of layers is used.
            initialization: The strategy to use for parameter initialization.
                If None, the strategy specified in the constructor is used.
            initialization_domain: The domain to use for parameter initialization.
                If None, the domain specified in the constructor is used.

        Returns:
            None
        """
        # Initializing params
        params_shape = (
            self._params_shape if repeat is None else [*self._params_shape, repeat]
        )
        # use existing strategy if not specified
        initialization = initialization or self._inialization_strategy
        initialization_domain = initialization_domain or self._initialization_domain

        def set_control_params(params: np.ndarray, value: float) -> np.ndarray:
            indices = self.pqc.get_control_indices(self.n_qubits)
            if indices is None:
                warnings.warn(
                    f"Specified {initialization} but circuit\
                    does not contain controlled rotation gates.\
                    Parameters are intialized randomly.",
                    UserWarning,
                )
            else:
                params[:, indices[0] : indices[1] : indices[2]] = (
                    np.ones_like(params[:, indices[0] : indices[1] : indices[2]])
                    * value
                )
            return params

        if initialization == "random":
            self.params: np.ndarray = rng.uniform(
                *initialization_domain, params_shape, requires_grad=True
            )
        elif initialization == "zeros":
            self.params: np.ndarray = np.zeros(params_shape, requires_grad=True)
        elif initialization == "pi":
            self.params: np.ndarray = np.ones(params_shape, requires_grad=True) * np.pi
        elif initialization == "zero-controlled":
            self.params: np.ndarray = rng.uniform(
                *initialization_domain, params_shape, requires_grad=True
            )
            self.params = set_control_params(self.params, 0)
        elif initialization == "pi-controlled":
            self.params: np.ndarray = rng.uniform(
                *initialization_domain, params_shape, requires_grad=True
            )
            self.params = set_control_params(self.params, np.pi)
        else:
            raise Exception("Invalid initialization method")

        log.info(
            f"Initialized parameters with shape {self.params.shape}\
            using strategy {initialization}."
        )

        # Initializing pulse params
        shape = (
            self._pulse_params_shape
            if repeat is None
            else (*self._pulse_params_shape, repeat)
        )
        self.pulse_params: np.ndarray = np.ones(shape, requires_grad=False)

        log.info(f"Initialized pulse parameters with shape {self.pulse_params.shape}.")

    def transform_input(self, inputs: np.ndarray, enc_params: Optional[np.ndarray]):
        """
        Transforms the input as in arXiv:2309.03279v2

        Args:
            inputs (np.ndarray): single input point of shape (1, n_input_feat)
            idx (int): feature index
            qubit (int): qubit on which to the encoding is being performed
            enc_params (np.ndarray): encoding weight vector of
                shape (n_qubits)

        Returns:
            np.ndarray: transformed input of shape (1,), linearly scaled by
            enc_params, ready for encoding
        """
        return inputs * enc_params

    def _iec(
        self,
        inputs: np.ndarray,
        data_reupload: np.ndarray,
        enc: Union[Callable, List[Callable]],
        enc_params: np.ndarray,
        noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None,
    ) -> None:
        """
        Creates an AngleEncoding using RX gates

        Args:
            inputs (np.ndarray): single input point of shape (1, n_input_feat)
            data_reupload (np.ndarray): Boolean array to indicate positions in
                the circuit for data re-uploading for the IEC, shape is
                (n_qubits, n_layers).
            enc: Callable or List[Callable]: encoding function or list of encoding
                functions
            enc_params (np.ndarray): encoding weight vector
                of shape [n_qubits, n_inputs]
            noise_params (Optional[Dict[str, Union[float, Dict[str, float]]]]):
                The noise parameters.
        Returns:
            None
        """
        # check for zero, because due to input validation, input cannot be none
        if self.remove_zero_encoding and not inputs.any():
            return

        for q in range(self.n_qubits):
            for idx in range(inputs.shape[1]):
                if data_reupload[q, idx]:
                    enc[idx](
                        self.transform_input(inputs[:, idx], enc_params[q, idx]),
                        wires=q,
                        noise_params=noise_params,
                    )

    def _circuit(
        self,
        params: np.ndarray,
        inputs: np.ndarray,
        pulse_params: np.ndarray = None,
        enc_params: Optional[np.ndarray] = None,
        gate_mode: str = "unitary",
    ) -> Union[float, np.ndarray]:
        # TODO: Is the shape of params below correct?
        """
        Creates a quantum circuit, optionally with noise or pulse simulation.

        Args:
            params (np.ndarray): weight vector of shape
                [n_layers, n_qubits*(n_params_per_layer+trainable_frequencies)]
            inputs (np.ndarray): input vector of size 1
            pulse_params Optional[np.ndarray]: pulse parameter scaler weights of shape
                [n_layers, n_pulse_params_per_layer]
            enc_params Optional[np.ndarray]: encoding weight vector
                of shape [n_qubits, n_inputs]
            gate_mode (str): Backend mode for gate execution. Can be
                "unitary" (default) or "pulse".
        Returns:
            Union[float, np.ndarray]: Expectation value of PauliZ(0)
                of the circuit if state_vector is False and expval is True,
                otherwise the density matrix of all qubits.
        """

        self._variational(
            params=params,
            inputs=inputs,
            pulse_params=pulse_params,
            enc_params=enc_params,
            gate_mode=gate_mode,
        )
        return self._observable()

    def _variational(
        self,
        params: np.ndarray,
        inputs: np.ndarray,
        pulse_params: Optional[np.ndarray] = None,
        enc_params: Optional[np.ndarray] = None,
        gate_mode: str = "unitary",
    ) -> None:
        """
        Builds the variational quantum circuit with state preparation,
        variational ansatz layers, and intertwined encoding layers.

        Args:
            params (np.ndarray): weight vector of shape
                [n_layers, n_qubits*(n_params_per_layer+trainable_frequencies)]
            inputs (np.ndarray): input vector of size 1
            pulse_params Optional[np.ndarray]: pulse parameter scaler weights of shape
                [n_layers, n_pulse_params_per_layer]
            enc_params Optional[np.ndarray]: encoding weight vector
                of shape [n_qubits, n_inputs]
            gate_mode (str): Backend mode for gate execution. Can be
                "unitary" (default) or "pulse".

        Returns:
            None
        """
        if enc_params is None:
            # TODO: Raise warning if trainable frequencies is True, or similar. I.e., no
            #   warning if user does not care for frequencies or enc_params
            if self.trainable_frequencies:
                warnings.warn(
                    "Explicit call to `_circuit` or `_variational` detected: "
                    "`enc_params` is None, using `self.enc_params` instead.",
                    RuntimeWarning,
                )
            enc_params = self.enc_params

        if pulse_params is None:
            if gate_mode == "pulse":
                warnings.warn(
                    "Explicit call to `_circuit` or `_variational` detected: "
                    "`pulse_params` is None, using `self.pulse_params` instead.",
                    RuntimeWarning,
                )
            pulse_params = self.pulse_params

        if self.noise_params is not None:
            self._apply_state_prep_noise()

        # state preparation
        for q in range(self.n_qubits):
            for _sp, sp_pulse_params in zip(self._sp, self.sp_pulse_params):
                _sp(
                    wires=q,
                    pulse_params=sp_pulse_params,
                    noise_params=self.noise_params,
                    gate_mode=gate_mode,
                )

        # circuit building
        for layer in range(0, self.n_layers):
            # ansatz layers
            self.pqc(
                params[layer],
                self.n_qubits,
                pulse_params=pulse_params[layer],
                noise_params=self.noise_params,
                gate_mode=gate_mode,
            )

            # encoding layers
            self._iec(
                inputs,
                data_reupload=self.data_reupload[layer],
                enc=self._enc,
                enc_params=enc_params,
                noise_params=self.noise_params,
            )

            # visual barrier
            if self.degree > 1:
                qml.Barrier(wires=list(range(self.n_qubits)), only_visual=True)

        # final ansatz layer
        if self.degree > 1:  # same check as in init
            self.pqc(
                params[-1],
                self.n_qubits,
                pulse_params=pulse_params[-1],
                noise_params=self.noise_params,
                gate_mode=gate_mode,
            )

        # channel noise
        if self.noise_params is not None:
            self._apply_general_noise()

    def _observable(self):
        # run mixed simualtion and get density matrix
        if self.execution_type == "density":
            return qml.density_matrix(wires=list(range(self.n_qubits)))
        elif self.execution_type == "state":
            return qml.state()
        # run default simulation and get expectation value
        elif self.execution_type == "expval":
            # n-local measurement
            if self.output_qubit == -1:
                return [qml.expval(qml.PauliZ(q)) for q in range(self.n_qubits)]
            # local measurement(s)
            elif isinstance(self.output_qubit, int):
                return qml.expval(qml.PauliZ(self.output_qubit))
            # parity measurenment
            elif isinstance(self.output_qubit, list):
                obs = qml.PauliZ(self.output_qubit[0])
                for out_qubit in self.output_qubit[1:]:
                    obs = obs @ qml.PauliZ(out_qubit)
                return qml.expval(obs)
            else:
                raise ValueError(
                    f"Invalid parameter `output_qubit`: {self.output_qubit}.\
                        Must be int, list or -1."
                )
        # run default simulation and get probs
        elif self.execution_type == "probs":
            if self.output_qubit == -1:
                return qml.probs(wires=list(range(self.n_qubits)))
            else:
                return qml.probs(wires=self.output_qubit)
        else:
            raise ValueError(f"Invalid execution_type: {self.execution_type}.")

    def _apply_state_prep_noise(self) -> None:
        """
        Applies a state preparation error on each qubit according to the
        probability for StatePreparation provided in the noise_params.
        """
        p = self.noise_params.get("StatePreparation", 0.0)
        for q in range(self.n_qubits):
            if p > 0:
                qml.BitFlip(p, wires=q)

    def _apply_general_noise(self) -> None:
        """
        Applies general types of noise the full circuit (in contrast to gate
        errors, applied directly at gate level, see Gates.Noise).

        Possible types of noise are:
            - AmplitudeDamping (specified through probability)
            - PhaseDamping (specified through probability)
            - ThermalRelaxation (specified through a dict, containing keys
                                 "t1", "t2", "t_factor")
            - Measurement (specified through probability)
        """
        amp_damp = self.noise_params.get("AmplitudeDamping", 0.0)
        phase_damp = self.noise_params.get("PhaseDamping", 0.0)
        thermal_relax = self.noise_params.get("ThermalRelaxation", 0.0)
        meas = self.noise_params.get("Measurement", 0.0)
        for q in range(self.n_qubits):
            if amp_damp > 0:
                qml.AmplitudeDamping(amp_damp, wires=q)
            if phase_damp > 0:
                qml.PhaseDamping(phase_damp, wires=q)
            if meas > 0:
                qml.BitFlip(meas, wires=q)
            if isinstance(thermal_relax, dict):
                t1 = thermal_relax["t1"]
                t2 = thermal_relax["t2"]
                t_factor = thermal_relax["t_factor"]
                circuit_depth = self._get_circuit_depth()
                tg = circuit_depth * t_factor
                qml.ThermalRelaxationError(1.0, t1, t2, tg, q)

    def _get_circuit_depth(self, inputs: Optional[np.ndarray] = None) -> int:
        """
        Obtain circuit depth for the model

        Args:
            inputs (Optional[np.ndarray]): The inputs, with which to call the
                circuit. Defaults to None.

        Returns:
            int: Circuit depth (longest path of gates in circuit.)
        """
        inputs = self._inputs_validation(inputs)
        spec_model = deepcopy(self)
        spec_model.noise_params = None  # remove noise
        specs = qml.specs(spec_model.circuit)(self.params, inputs)

        return specs["resources"].depth

    def draw(self, inputs=None, figure="text", *args, **kwargs):
        """
        Draws the quantum circuit using the specified visualization method.

        Args:
            inputs (Optional[np.ndarray]): Input vector for the circuit. If None,
                the default inputs are used.
            figure (str, optional): The type of figure to generate. Must be one of
                'text', 'mpl', or 'tikz'. Defaults to 'text'.
        Returns:
            Either a string, matplotlib figure or TikzFigure object (similar to string)
            depending on the chosen visualization.
        *args:
            Additional arguments to be passed to the visualization method.
        **kwargs:
            Additional keyword arguments to be passed to the visualization method.
            Can include `pulse_params`, `gate_mode`, `enc_params`, or `noise_params`.

        Raises:
            AssertionError: If the 'figure' argument is not one of the accepted values.
        """

        if not isinstance(self.circuit, qml.QNode):
            # TODO: throws strange argument error if not catched
            return ""

        assert figure in [
            "text",
            "mpl",
            "tikz",
        ], f"Invalid figure: {figure}. Must be 'text', 'mpl' or 'tikz'."

        inputs = self._inputs_validation(inputs)

        if figure == "mpl":
            result = qml.draw_mpl(self.circuit)(
                params=self.params,
                inputs=inputs,
                *args,
                **kwargs,
            )
        elif figure == "tikz":
            result = QuanTikz.build(
                self.circuit,
                params=self.params,
                inputs=inputs,
                *args,
                **kwargs,
            )
        else:
            result = qml.draw(self.circuit)(params=self.params, inputs=inputs)
        return result

    def __repr__(self) -> str:
        return self.draw(figure="text")

    def __str__(self) -> str:
        return self.draw(figure="text")

    def _params_validation(self, params) -> np.ndarray:
        """
        Sets the parameters when calling the quantum circuit.

        Args:
            params (np.ndarray): The parameters used for the call.

        Returns:
            np.ndarray: Validated parameters.
        """
        if params is None:
            params = self.params
        else:
            if numpy_boxes.ArrayBox == type(params):
                self.params = params._value
            else:
                self.params = params

        # Get rid of extra dimension
        if len(params.shape) == 3 and params.shape[2] == 1:
            params = params[:, :, 0]

        return params

    def _pulse_params_validation(self, pulse_params) -> np.ndarray:
        """
        Sets the pulse parameters when calling the quantum circuit.

        Args:
            pulse_params (np.ndarray): The pulse parameter scalers used for the call.

        Returns:
            np.ndarray: Validated pulse parameters, with `requires_grad` set according
            to the current `gate_mode`.
        """
        if pulse_params is None:
            pulse_params = self.pulse_params
        else:
            if isinstance(pulse_params, numpy_boxes.ArrayBox):
                self.pulse_params = pulse_params._value
            else:
                self.pulse_params = pulse_params

        # flip requires_grad depending on current gate_mode
        if self.gate_mode == "pulse":
            self.pulse_params = np.array(self.pulse_params, requires_grad=True)
        else:
            self.pulse_params = np.array(self.pulse_params, requires_grad=False)

        return pulse_params

    def _enc_params_validation(self, enc_params) -> np.ndarray:
        """
        Sets the encoding parameters when calling the quantum circuit

        Args:
            enc_params (np.ndarray): The encoding parameters used for the call
        """
        if enc_params is None:
            enc_params = self.enc_params
        else:
            if isinstance(enc_params, numpy_boxes.ArrayBox):
                if self.trainable_frequencies:
                    self.enc_params = enc_params._value
                else:
                    self.enc_params = np.array(
                        enc_params._value, requires_grad=self.trainable_frequencies
                    )
            else:
                if self.trainable_frequencies:
                    self.enc_params = enc_params
                else:
                    self.enc_params = np.array(
                        enc_params, requires_grad=self.trainable_frequencies
                    )

        if len(enc_params.shape) == 1 and self.n_input_feat == 1:
            enc_params = enc_params.reshape(-1, 1)
        elif len(enc_params.shape) == 1 and self.n_input_feat > 1:
            raise ValueError(
                f"Input dimension {self.n_input_feat} >1 but \
                `enc_params` has shape {enc_params.shape}"
            )

        return enc_params

    def _inputs_validation(
        self, inputs: Union[None, List, float, int, np.ndarray]
    ) -> np.ndarray:
        """
        Validate the inputs to be a 2D numpy array of shape (batch_size, n_inputs).

        Args:
            inputs (Union[None, List, float, int, np.ndarray]): The input to validate.

        Returns:
            np.ndarray: The validated input.
        """
        if inputs is None:
            # initialize to zero
            inputs = np.array([[0] * self.n_input_feat])
        elif isinstance(inputs, List):
            inputs = np.stack(inputs)
        elif isinstance(inputs, float) or isinstance(inputs, int):
            inputs = np.array([inputs])

        if len(inputs.shape) <= 1:
            if self.n_input_feat == 1:
                # add a batch dimension
                inputs = inputs.reshape(-1, 1)
            else:
                if inputs.shape[0] == self.n_input_feat:
                    inputs = inputs.reshape(1, -1)
                else:
                    inputs = inputs.reshape(-1, 1)
                    inputs = inputs.repeat(self.n_input_feat, axis=1)
                    warnings.warn(
                        f"Expected {self.n_input_feat} inputs, but {inputs.shape[0]} "
                        "was provided, replicating input for all input features.",
                        UserWarning,
                    )
        else:
            if inputs.shape[1] != self.n_input_feat:
                raise ValueError(
                    f"Wrong number of inputs provided. Expected {self.n_input_feat} "
                    f"inputs, but input has shape {inputs.shape}."
                )

        return inputs

    @staticmethod
    def _parallel_f(
        procnum,
        result,
        f,
        batch_size,
        params: np.ndarray,
        pulse_params: np.ndarray,
        inputs: np.ndarray,
        batch_shape,
        enc_params,
        gate_mode: str,
    ):
        """
        Helper function for parallelizing a function f over parameters.
        Sices the batch dimension based on the procnum and batch size.

        Args:
            procnum: The process number.
            result: The result array.
            f: The function to be parallelized.
            batch_size: The batch size.
            params: The parameters array.
            pulse_params (np.ndarray): Pulse parameter scalers for pulse-mode gates.
            inputs: The inputs array.
            enc_params: The encoding parameters array.
            gate_mode (str): Mode for gate execution ("unitary" or "pulse").
        """
        min_idx = max(procnum * batch_size, 0)

        if batch_shape[0] > 1:
            max_idx = min((procnum + 1) * batch_size, inputs.shape[0])
            inputs = inputs[min_idx:max_idx]
        if batch_shape[1] > 1:
            max_idx = min((procnum + 1) * batch_size, params.shape[2])
            params = params[:, :, min_idx:max_idx]

        result[procnum] = f(
            params=params,
            pulse_params=pulse_params,
            inputs=inputs,
            enc_params=enc_params,
            gate_mode=gate_mode,
        )

    def _mp_executor(self, f, params, pulse_params, inputs, enc_params, gate_mode):
        """
        Execute a function f in parallel over parameters.

        Args:
            f: A function that takes two arguments, params and inputs,
                and returns a numpy array.
            params: A 3D numpy array of parameters where the first dimension is
                the layer index, the second dimension is the parameter index in
                the layer, and the third dimension is the sample index.
            pulse_params (np.ndarray): array of pulse parameter scalers for pulse-mode
                gates.
            inputs: A 2D numpy array of inputs where the first dimension is
                the sample index and the second dimension is the input feature index.
            enc_params: A 1D numpy array of encoding parameters where the dimension is
                the qubit index.
            gate_mode (str): Mode for gate execution ("unitary" or "pulse").

        Returns:
            A numpy array of the output of f applied to each batch of
            samples in params, enc_params, and inputs.
        """
        n_processes = 1
        # batches available?
        combined_batch_size = math.prod(self.batch_shape)
        if (
            combined_batch_size > 1
            and self.mp_threshold > 0
            and combined_batch_size > self.mp_threshold
        ):
            n_processes = math.ceil(combined_batch_size / self.mp_threshold)
        # check if single process
        if n_processes == 1:
            if self.mp_threshold > 0:
                warnings.warn(
                    f"Multiprocessing threshold {self.mp_threshold}>0, but using \
                    single process, because {combined_batch_size} samples per batch.",
                )
            result = f(
                params=params,
                pulse_params=pulse_params,
                inputs=inputs,
                enc_params=enc_params,
                gate_mode=gate_mode,
            )
        else:
            log.info(f"Using {n_processes} processes")
            mpp = MultiprocessingPool(
                target=Model._parallel_f,
                n_processes=n_processes,
                cpu_scaler=self.cpu_scaler,
                batch_size=self.mp_threshold,
                f=f,
                params=params,
                pulse_params=pulse_params,
                enc_params=enc_params,
                inputs=inputs,
                gate_mode=gate_mode,
                batch_shape=self.batch_shape,
            )
            return_dict = mpp.spawn()

            # TODO: the following code could use some optimization
            result = [None] * len(return_dict)
            for k, v in return_dict.items():
                result[k] = v

            result = np.concat(result, axis=1 if self.execution_type == "expval" else 0)
        return result

    def _assimilate_batch(self, inputs, params, pulse_params):
        batch_shape = (
            inputs.shape[0],
            params.shape[2] if len(params.shape) == 3 else 1,
        )

        if (
            batch_shape[1] != 1
            and batch_shape[0] != batch_shape[1]
            and batch_shape[0] > 1
        ):
            # the following code does some dirty reshaping
            # TODO: optimize but be aware of the rabbit hole
            # key is to get the right "order" in which we repeat

            # [BI,D] -> [BPxBI,D]
            inputs = np.repeat(inputs, batch_shape[1], axis=0)

            # this is a tricky one, essentially we want to get
            # [L,Q,BP] -> [L,Q,BI,BP] -> [L,Q,BPxBI]
            params = np.repeat(
                params[:, :, np.newaxis, :], batch_shape[0], axis=2
            ).reshape([*params.shape[:-1], np.prod(batch_shape)])

            pulse_params = np.repeat(
                pulse_params[:, :, np.newaxis, :], batch_shape[0], axis=2
            ).reshape([*pulse_params.shape[:-1], np.prod(batch_shape)])

        return inputs, params, pulse_params, batch_shape

    def _requires_density(self):
        """
        Checks if the current model requires density matrix simulation or not
        based on the noise_params variable and the execution type

        Returns:
            bool: True if model requires density simulation
        """
        if self.execution_type == "density":
            return True

        if self.noise_params is not None:
            coherent_noise = ["GateError"]
            for k, v in self.noise_params.items():
                if k in coherent_noise:
                    continue
                if v is not None and v > 0:
                    return True
        return False

    def __call__(
        self,
        params: Optional[np.ndarray] = None,
        inputs: Optional[np.ndarray] = None,
        pulse_params: Optional[np.ndarray] = None,
        enc_params: Optional[np.ndarray] = None,
        noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None,
        cache: Optional[bool] = False,
        execution_type: Optional[str] = None,
        force_mean: bool = False,
        gate_mode: str = "unitary",
    ) -> np.ndarray:
        """
        Perform a forward pass of the quantum circuit with optional noise or
        pulse level simulation.

        Args:
            params (Optional[np.ndarray]): Weight vector of shape
                [n_layers, n_qubits*n_params_per_layer].
                If None, model internal parameters are used.
            inputs (Optional[np.ndarray]): Input vector of shape [1].
                If None, zeros are used.
            pulse_params (Optional[np.ndarray]): Pulse parameter scalers for pulse-mode
                gates.
            enc_params (Optional[np.ndarray]): Weight vector of shape
                [n_qubits, n_input_features]. If None, model internal encoding
                parameters are used.
            noise_params (Optional[Dict[str, float]], optional): The noise parameters.
                Defaults to None which results in the last
                set noise parameters being used.
            cache (Optional[bool], optional): Whether to cache the results.
                Defaults to False.
            execution_type (str, optional): The type of execution.
                Must be one of 'expval', 'density', or 'probs'.
                Defaults to None which results in the last set execution type
                being used.
            force_mean (bool, optional): Whether to average
                when performing n-local measurements.
                Defaults to False.
            gate_mode (str, optional): Gate backend mode ("unitary" or "pulse").
                Defaults to "unitary".

        Returns:
            np.ndarray: The output of the quantum circuit.
                The shape depends on the execution_type.
                - If execution_type is 'expval', returns an ndarray of shape
                    (1,) if output_qubit is -1, else (len(output_qubit),).
                - If execution_type is 'density', returns an ndarray
                    of shape (2**n_qubits, 2**n_qubits).
                - If execution_type is 'probs', returns an ndarray
                    of shape (2**n_qubits,) if output_qubit is -1, else
                    (2**len(output_qubit),).
        """
        # Call forward method which handles the actual caching etc.
        return self._forward(
            params=params,
            inputs=inputs,
            pulse_params=pulse_params,
            enc_params=enc_params,
            noise_params=noise_params,
            cache=cache,
            execution_type=execution_type,
            force_mean=force_mean,
            gate_mode=gate_mode,
        )

    def _forward(
        self,
        params: Optional[np.ndarray] = None,
        inputs: Optional[np.ndarray] = None,
        pulse_params: Optional[np.ndarray] = None,
        enc_params: Optional[np.ndarray] = None,
        noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None,
        cache: Optional[bool] = False,
        execution_type: Optional[str] = None,
        force_mean: bool = False,
        gate_mode: str = "unitary",
    ) -> np.ndarray:
        """
        Perform a forward pass of the quantum circuit.

        Args:
            params (Optional[np.ndarray]): Weight vector of shape
                [n_layers, n_qubits*n_params_per_layer].
                If None, model internal parameters are used.
            inputs (Optional[np.ndarray]): Input vector of shape [1].
                If None, zeros are used.
            pulse_params (Optional[np.ndarray]): Pulse parameter scalers for pulse-mode
                gates.
            enc_params (Optional[np.ndarray]): Weight vector of shape
                [n_qubits, n_input_features]. If None, model internal encoding
                parameters are used.
            noise_params (Optional[Dict[str, float]], optional): The noise parameters.
                Defaults to None which results in the last
                set noise parameters being used.
            cache (Optional[bool], optional): Whether to cache the results.
                Defaults to False.
            execution_type (str, optional): The type of execution.
                Must be one of 'expval', 'density', or 'probs'.
                Defaults to None which results in the last set execution type
                being used.
            force_mean (bool, optional): Whether to average
                when performing n-local measurements.
                Defaults to False.
            gate_mode (str, optional): Gate backend mode ("unitary" or "pulse").
                Defaults to "unitary".


        Returns:
            np.ndarray: The output of the quantum circuit.
                The shape depends on the execution_type.
                - If execution_type is 'expval', returns an ndarray of shape
                    (1,) if output_qubit is -1, else (len(output_qubit),).
                - If execution_type is 'density', returns an ndarray
                    of shape (2**n_qubits, 2**n_qubits).
                - If execution_type is 'probs', returns an ndarray
                    of shape (2**n_qubits,) if output_qubit is -1, else
                    (2**len(output_qubit),).

        Raises:
            NotImplementedError: If the number of shots is not None or if the
                expectation value is True.
            ValueError:
                - If `pulse_params` are provided but `gate_mode` is not "pulse".
                - If `noise_params` are provided while `gate_mode` is "pulse" (noise
                not supported in pulse mode).
        """
        # set the parameters as object attributes
        if noise_params is not None:
            self.noise_params = noise_params
        if execution_type is not None:
            self.execution_type = execution_type
        self.gate_mode = gate_mode

        # consistency checks
        if pulse_params is not None and gate_mode != "pulse":
            raise ValueError(
                "pulse_params were provided but gate_mode is not 'pulse'. "
                "Either switch gate_mode='pulse' or do not pass pulse_params."
            )

        if noise_params is not None and gate_mode == "pulse":
            raise ValueError(
                "Noise is not supported in 'pulse' gate_mode. "
                "Either remove noise_params or use gate_mode='unitary'."
            )

        params = self._params_validation(params)
        pulse_params = self._pulse_params_validation(pulse_params)
        inputs = self._inputs_validation(inputs)
        enc_params = self._enc_params_validation(enc_params)

        inputs, params, pulse_params, self.batch_shape = self._assimilate_batch(
            inputs,
            params,
            pulse_params,
        )
        # the qasm representation contains the bound parameters,
        # thus it is ok to hash that
        hs = hashlib.md5(
            repr(
                {
                    "n_qubits": self.n_qubits,
                    "n_layers": self.n_layers,
                    "pqc": self.pqc.__class__.__name__,
                    "dru": self.data_reupload,
                    "params": self.params,  # use safe-params
                    "pulse_params": self.pulse_params,
                    "enc_params": self.enc_params,
                    "noise_params": self.noise_params,
                    "execution_type": self.execution_type,
                    "inputs": inputs,
                    "output_qubit": self.output_qubit,
                }
            ).encode("utf-8")
        ).hexdigest()

        result: Optional[np.ndarray] = None
        if cache:
            name: str = f"pqc_{hs}.npy"

            cache_folder: str = ".cache"
            if not os.path.exists(cache_folder):
                os.mkdir(cache_folder)

            file_path: str = os.path.join(cache_folder, name)

            if os.path.isfile(file_path):
                result = np.load(file_path)

        if result is None:
            # if density matrix requested or noise params used
            if self._requires_density():
                result = self._mp_executor(
                    f=self.circuit_mixed,
                    params=params,  # use arraybox params
                    pulse_params=pulse_params,
                    inputs=inputs,
                    enc_params=enc_params,
                    gate_mode=gate_mode,
                )
            else:
                if not isinstance(self.circuit, qml.QNode):
                    result = self.circuit(
                        inputs=inputs,
                    )
                else:
                    result = self._mp_executor(
                        f=self.circuit,
                        params=params,  # use arraybox params
                        pulse_params=pulse_params,
                        inputs=inputs,
                        enc_params=enc_params,
                        gate_mode=gate_mode,
                    )

        if isinstance(result, list):
            result = np.stack(result)

        if self.execution_type == "expval" and force_mean and self.output_qubit == -1:
            # exception for torch layer because it swaps batch and output dimension
            if not isinstance(self.circuit, qml.QNode):
                result = result.mean(axis=-1)
            else:
                result = result.mean(axis=0)
        elif self.execution_type == "probs" and force_mean and self.output_qubit == -1:
            # exception for torch layer because it swaps batch and output dimension
            if not isinstance(self.circuit, qml.QNode):
                result = result[..., -1].sum(axis=-1)
            else:
                result = result[1:, ...].sum(axis=0)

        if self.batch_shape[0] > 1 and self.batch_shape[1] > 1:
            result = result.reshape(-1, *self.batch_shape)

        result = result.squeeze()

        if cache:
            np.save(file_path, result)

        return result

execution_type property writable

Gets the execution type of the model.

Returns:

Name	Type	Description
str	str	The execution type, one of 'density', 'expval', or 'probs'.

noise_params property writable

Gets the noise parameters of the model.

Returns:

Type	Description
Optional[Dict[str, Union[float, Dict[str, float]]]]	Optional[Dict[str, float]]: A dictionary of
Optional[Dict[str, Union[float, Dict[str, float]]]]	noise parameters or None if not set.

shots property writable

Gets the number of shots to use for the quantum device.

Returns:

Type	Description
Optional[int]	Optional[int]: The number of shots.

__call__(params=None, inputs=None, pulse_params=None, enc_params=None, noise_params=None, cache=False, execution_type=None, force_mean=False, gate_mode='unitary')

Perform a forward pass of the quantum circuit with optional noise or pulse level simulation.

Parameters:

Name	Type	Description	Default
params	Optional[ndarray]	Weight vector of shape [n_layers, n_qubits*n_params_per_layer]. If None, model internal parameters are used.	None
inputs	Optional[ndarray]	Input vector of shape [1]. If None, zeros are used.	None
pulse_params	Optional[ndarray]	Pulse parameter scalers for pulse-mode gates.	None
enc_params	Optional[ndarray]	Weight vector of shape [n_qubits, n_input_features]. If None, model internal encoding parameters are used.	None
noise_params	Optional[Dict[str, float]]	The noise parameters. Defaults to None which results in the last set noise parameters being used.	None
cache	Optional[bool]	Whether to cache the results. Defaults to False.	False
execution_type	str	The type of execution. Must be one of 'expval', 'density', or 'probs'. Defaults to None which results in the last set execution type being used.	None
force_mean	bool	Whether to average when performing n-local measurements. Defaults to False.	False
gate_mode	str	Gate backend mode ("unitary" or "pulse"). Defaults to "unitary".	'unitary'

Returns:

Type

Description

ndarray

np.ndarray: The output of the quantum circuit. The shape depends on the execution_type. - If execution_type is 'expval', returns an ndarray of shape (1,) if output_qubit is -1, else (len(output_qubit),). - If execution_type is 'density', returns an ndarray of shape (2n_qubits, 2n_qubits). - If execution_type is 'probs', returns an ndarray of shape (2n_qubits,) if output_qubit is -1, else (2len(output_qubit),).

Source code in qml_essentials/model.py

def __call__(
    self,
    params: Optional[np.ndarray] = None,
    inputs: Optional[np.ndarray] = None,
    pulse_params: Optional[np.ndarray] = None,
    enc_params: Optional[np.ndarray] = None,
    noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None,
    cache: Optional[bool] = False,
    execution_type: Optional[str] = None,
    force_mean: bool = False,
    gate_mode: str = "unitary",
) -> np.ndarray:
    """
    Perform a forward pass of the quantum circuit with optional noise or
    pulse level simulation.

    Args:
        params (Optional[np.ndarray]): Weight vector of shape
            [n_layers, n_qubits*n_params_per_layer].
            If None, model internal parameters are used.
        inputs (Optional[np.ndarray]): Input vector of shape [1].
            If None, zeros are used.
        pulse_params (Optional[np.ndarray]): Pulse parameter scalers for pulse-mode
            gates.
        enc_params (Optional[np.ndarray]): Weight vector of shape
            [n_qubits, n_input_features]. If None, model internal encoding
            parameters are used.
        noise_params (Optional[Dict[str, float]], optional): The noise parameters.
            Defaults to None which results in the last
            set noise parameters being used.
        cache (Optional[bool], optional): Whether to cache the results.
            Defaults to False.
        execution_type (str, optional): The type of execution.
            Must be one of 'expval', 'density', or 'probs'.
            Defaults to None which results in the last set execution type
            being used.
        force_mean (bool, optional): Whether to average
            when performing n-local measurements.
            Defaults to False.
        gate_mode (str, optional): Gate backend mode ("unitary" or "pulse").
            Defaults to "unitary".

    Returns:
        np.ndarray: The output of the quantum circuit.
            The shape depends on the execution_type.
            - If execution_type is 'expval', returns an ndarray of shape
                (1,) if output_qubit is -1, else (len(output_qubit),).
            - If execution_type is 'density', returns an ndarray
                of shape (2**n_qubits, 2**n_qubits).
            - If execution_type is 'probs', returns an ndarray
                of shape (2**n_qubits,) if output_qubit is -1, else
                (2**len(output_qubit),).
    """
    # Call forward method which handles the actual caching etc.
    return self._forward(
        params=params,
        inputs=inputs,
        pulse_params=pulse_params,
        enc_params=enc_params,
        noise_params=noise_params,
        cache=cache,
        execution_type=execution_type,
        force_mean=force_mean,
        gate_mode=gate_mode,
    )

__init__(n_qubits, n_layers, circuit_type='No_Ansatz', data_reupload=True, state_preparation=None, encoding=Gates.RX, trainable_frequencies=False, initialization='random', initialization_domain=[0, 2 * np.pi], output_qubit=-1, shots=None, random_seed=1000, as_pauli_circuit=False, remove_zero_encoding=True, mp_threshold=-1)

Initialize the quantum circuit model. Parameters will have the shape [impl_n_layers, parameters_per_layer] where impl_n_layers is the number of layers provided and added by one depending if data_reupload is True and parameters_per_layer is given by the chosen ansatz.

The model is initialized with the following parameters as defaults: - noise_params: None - execution_type: "expval" - shots: None

Parameters:

Name	Type	Description	Default
n_qubits	int	The number of qubits in the circuit.	required
n_layers	int	The number of layers in the circuit.	required
circuit_type	(str, Circuit)	The type of quantum circuit to use. If None, defaults to "no_ansatz".	'No_Ansatz'
data_reupload	Union[bool, List[bool], List[List[bool]]]	Whether to reupload data to the quantum device on each layer and qubit. Detailed re-uploading instructions can be given as a list/array of 0/False and 1/True with shape (n_qubits, n_layers) to specify where to upload the data. Defaults to True for applying data re-uploading to the full circuit.	True
encoding	Union[str, Callable, List[str], List[Callable]]	The unitary to use for encoding the input data. Can be a string (e.g. "RX") or a callable (e.g. qml.RX). Defaults to qml.RX. If input is multidimensional it is assumed to be a list of unitaries or a list of strings.	RX
trainable_frequencies	bool	Sets trainable encoding parameters for trainable frequencies. Defaults to False.	False
initialization	str	The strategy to initialize the parameters. Can be "random", "zeros", "zero-controlled", "pi", or "pi-controlled". Defaults to "random".	'random'
output_qubit	(List[int], int)	The index of the output qubit (or qubits). When set to -1 all qubits are measured, or a global measurement is conducted, depending on the execution type.	-1
shots	Optional[int]	The number of shots to use for the quantum device. Defaults to None.	None
random_seed	int	seed for the random number generator in initialization is "random" and for random noise parameters. Defaults to 1000.	1000
as_pauli_circuit	bool	whether the circuit is transformed to a Pauli-Clifford circuit as described by Nemkov et al. (https://doi.org/10.1103/PhysRevA.108.032406), which is required for analytical Fourier coefficient computation. Defaults to False.	False
remove_zero_encoding	bool	whether to remove the zero encoding from the circuit. Defaults to True.	True
mp_threshold	int	threshold above which the parameter batch dimension is split across multiple processes. Defaults to -1.	-1

Returns:

Type	Description
None	None

Source code in qml_essentials/model.py

def __init__(
    self,
    n_qubits: int,
    n_layers: int,
    circuit_type: Union[str, Circuit] = "No_Ansatz",
    data_reupload: Union[bool, List[bool], List[List[bool]]] = True,
    state_preparation: Union[str, Callable, List[str], List[Callable]] = None,
    encoding: Union[str, Callable, List[str], List[Callable]] = Gates.RX,
    trainable_frequencies: bool = False,
    initialization: str = "random",
    initialization_domain: List[float] = [0, 2 * np.pi],
    output_qubit: Union[List[int], int] = -1,
    shots: Optional[int] = None,
    random_seed: int = 1000,
    as_pauli_circuit: bool = False,
    remove_zero_encoding: bool = True,
    mp_threshold: int = -1,
) -> None:
    """
    Initialize the quantum circuit model.
    Parameters will have the shape [impl_n_layers, parameters_per_layer]
    where impl_n_layers is the number of layers provided and added by one
    depending if data_reupload is True and parameters_per_layer is given by
    the chosen ansatz.

    The model is initialized with the following parameters as defaults:
    - noise_params: None
    - execution_type: "expval"
    - shots: None

    Args:
        n_qubits (int): The number of qubits in the circuit.
        n_layers (int): The number of layers in the circuit.
        circuit_type (str, Circuit): The type of quantum circuit to use.
            If None, defaults to "no_ansatz".
        data_reupload (Union[bool, List[bool], List[List[bool]]], optional):
            Whether to reupload data to the quantum device on each
            layer and qubit. Detailed re-uploading instructions can be given
            as a list/array of 0/False and 1/True with shape (n_qubits,
            n_layers) to specify where to upload the data. Defaults to True
            for applying data re-uploading to the full circuit.
        encoding (Union[str, Callable, List[str], List[Callable]], optional):
            The unitary to use for encoding the input data. Can be a string
            (e.g. "RX") or a callable (e.g. qml.RX). Defaults to qml.RX.
            If input is multidimensional it is assumed to be a list of
            unitaries or a list of strings.
        trainable_frequencies (bool, optional):
            Sets trainable encoding parameters for trainable frequencies.
            Defaults to False.
        initialization (str, optional): The strategy to initialize the parameters.
            Can be "random", "zeros", "zero-controlled", "pi", or "pi-controlled".
            Defaults to "random".
        output_qubit (List[int], int, optional): The index of the output
            qubit (or qubits). When set to -1 all qubits are measured, or a
            global measurement is conducted, depending on the execution
            type.
        shots (Optional[int], optional): The number of shots to use for
            the quantum device. Defaults to None.
        random_seed (int, optional): seed for the random number generator
            in initialization is "random" and for random noise parameters.
            Defaults to 1000.
        as_pauli_circuit (bool, optional): whether the circuit is
            transformed to a Pauli-Clifford circuit as described by Nemkov
            et al. (https://doi.org/10.1103/PhysRevA.108.032406), which is
            required for analytical Fourier coefficient computation.
            Defaults to False.
        remove_zero_encoding (bool, optional): whether to
            remove the zero encoding from the circuit. Defaults to True.
        mp_threshold (int, optional): threshold above which the parameter
            batch dimension is split across multiple processes.
            Defaults to -1.

    Returns:
        None
    """
    # Initialize default parameters needed for circuit evaluation
    self.noise_params: Optional[Dict[str, Union[float, Dict[str, float]]]] = None
    self.execution_type: Optional[str] = "expval"
    self.shots = shots
    self.remove_zero_encoding = remove_zero_encoding
    self.mp_threshold = mp_threshold
    self.n_qubits: int = n_qubits
    self.n_layers: int = n_layers
    self.trainable_frequencies: bool = trainable_frequencies

    if isinstance(output_qubit, list):
        assert (
            len(output_qubit) <= n_qubits
        ), f"Size of output_qubit {len(output_qubit)} cannot be\
        larger than number of qubits {n_qubits}."
    self.output_qubit: Union[List[int], int] = output_qubit

    # Initialize rng in Gates
    Gates.init_rng(random_seed)

    # --- State Preparation ---
    # first check if we have a str, list or callable
    if isinstance(state_preparation, str):
        # if str, use the pennylane fct
        self._sp = [getattr(Gates, f"{state_preparation}")]
    elif isinstance(state_preparation, list):
        # if list, check if str or callable
        if isinstance(state_preparation[0], str):
            self._sp = [getattr(Gates, f"{sp}") for sp in state_preparation]
        else:
            self._sp = state_preparation
    elif state_preparation is None:
        self._sp = [lambda *args, **kwargs: None]
    else:
        # default to callable
        self._sp = [state_preparation]

    # prepare corresponding pulse parameters (always optimized pulses)
    self.sp_pulse_params = []
    for sp in self._sp:
        sp_name = sp.__name__ if hasattr(sp, "__name__") else str(sp)

        if sp_name in pinfo.OPTIMIZED_PULSES:
            params = np.array(pinfo.optimized_params(sp_name), requires_grad=False)
            self.sp_pulse_params.append(params)
        else:
            # gate has no pulse parametrization
            self.sp_pulse_params.append(None)

    # --- Encoding ---
    # first check if we have a str, list or callable
    if isinstance(encoding, str):
        # if str, use the pennylane fct
        self._enc = [getattr(Gates, f"{encoding}")]
    elif isinstance(encoding, list):
        # if list, check if str or callable
        if isinstance(encoding[0], str):
            self._enc = [getattr(Gates, f"{enc}") for enc in encoding]
        else:
            self._enc = encoding
    else:
        # default to callable
        self._enc = [encoding]

    # Number of possible inputs
    self.n_input_feat = len(self._enc)
    log.info(f"Number of input features: {self.n_input_feat}")

    # Trainable frequencies, default initialization as in arXiv:2309.03279v2
    self.enc_params = np.ones(
        (self.n_qubits, self.n_input_feat), requires_grad=trainable_frequencies
    )

    # --- Data-Reuploading ---
    # Process data reuploading strategy and set degree
    if not isinstance(data_reupload, bool):
        if not isinstance(data_reupload, np.ndarray):
            data_reupload = np.array(data_reupload)
        if data_reupload.shape == (
            n_layers,
            n_qubits,
        ):
            data_reupload = data_reupload.reshape(*data_reupload.shape, 1)
            data_reupload = np.repeat(data_reupload, self.n_input_feat, axis=2)

        assert data_reupload.shape == (
            n_layers,
            n_qubits,
            self.n_input_feat,
        ), f"Data reuploading array has wrong shape. \
            Expected {(n_layers, n_qubits)} or\
            {(n_layers, n_qubits, self.n_input_feat)},\
            got {data_reupload.shape}."

        log.debug(f"Data reuploading array:\n{data_reupload}")
    else:
        if data_reupload:
            impl_n_layers: int = (
                n_layers + 1
            )  # we need L+1 according to Schuld et al.
            data_reupload = np.ones((n_layers, n_qubits, self.n_input_feat))
            log.debug("Full data reuploading.")
        else:
            impl_n_layers: int = n_layers
            data_reupload = np.zeros((n_layers, n_qubits, self.n_input_feat))
            data_reupload[0][0] = 1
            log.debug("No data reuploading.")

    # convert to boolean values
    self.data_reupload = data_reupload.astype(bool)
    self.frequencies = [
        np.count_nonzero(self.data_reupload[..., i])
        for i in range(self.n_input_feat)
    ]

    if self.degree > 1:
        impl_n_layers: int = n_layers + 1  # we need L+1 according to Schuld et al.
    else:
        impl_n_layers = n_layers
    log.info(f"Number of implicit layers: {impl_n_layers}.")

    # --- Ansatz ---
    # only weak check for str. We trust the user to provide sth useful
    if isinstance(circuit_type, str):
        self.pqc: Callable[[Optional[np.ndarray], int], int] = getattr(
            Ansaetze, circuit_type or "No_Ansatz"
        )()
    else:
        self.pqc = circuit_type()
    log.info(f"Using Ansatz {circuit_type}.")

    # calculate the shape of the parameter vector here, we will re-use this in init.
    params_per_layer = self.pqc.n_params_per_layer(self.n_qubits)
    self._params_shape: Tuple[int, int] = (impl_n_layers, params_per_layer)
    log.info(f"Parameters per layer: {params_per_layer}")

    pulse_params_per_layer = self.pqc.n_pulse_params_per_layer(self.n_qubits)
    self._pulse_params_shape: Tuple[int, int] = (
        impl_n_layers,
        pulse_params_per_layer,
    )

    self.batch_shape = (1, 1)
    # this will also be re-used in the init method,
    # however, only if nothing is provided
    self._inialization_strategy = initialization
    self._initialization_domain = initialization_domain

    # ..here! where we only require a rng
    self.initialize_params(np.random.default_rng(random_seed))

    # Initialize two circuits, one with the default device and
    # one with the mixed device
    # which allows us to later route depending on the state_vector flag
    self.as_pauli_circuit = as_pauli_circuit

    self.circuit_mixed: qml.QNode = qml.QNode(
        self._circuit,
        qml.device("default.mixed", shots=self.shots, wires=self.n_qubits),
        interface="autograd" if self.shots is not None else "auto",
        diff_method="parameter-shift" if self.shots is not None else "best",
    )

draw(inputs=None, figure='text', *args, **kwargs)

Draws the quantum circuit using the specified visualization method.

Parameters:

Name	Type	Description	Default
inputs	Optional[ndarray]	Input vector for the circuit. If None, the default inputs are used.	None
figure	str	The type of figure to generate. Must be one of 'text', 'mpl', or 'tikz'. Defaults to 'text'.	'text'

Returns: Either a string, matplotlib figure or TikzFigure object (similar to string) depending on the chosen visualization. args: Additional arguments to be passed to the visualization method. *kwargs: Additional keyword arguments to be passed to the visualization method. Can include pulse_params, gate_mode, enc_params, or noise_params.

Raises:

Type	Description
AssertionError	If the 'figure' argument is not one of the accepted values.

Source code in qml_essentials/model.py

def draw(self, inputs=None, figure="text", *args, **kwargs):
    """
    Draws the quantum circuit using the specified visualization method.

    Args:
        inputs (Optional[np.ndarray]): Input vector for the circuit. If None,
            the default inputs are used.
        figure (str, optional): The type of figure to generate. Must be one of
            'text', 'mpl', or 'tikz'. Defaults to 'text'.
    Returns:
        Either a string, matplotlib figure or TikzFigure object (similar to string)
        depending on the chosen visualization.
    *args:
        Additional arguments to be passed to the visualization method.
    **kwargs:
        Additional keyword arguments to be passed to the visualization method.
        Can include `pulse_params`, `gate_mode`, `enc_params`, or `noise_params`.

    Raises:
        AssertionError: If the 'figure' argument is not one of the accepted values.
    """

    if not isinstance(self.circuit, qml.QNode):
        # TODO: throws strange argument error if not catched
        return ""

    assert figure in [
        "text",
        "mpl",
        "tikz",
    ], f"Invalid figure: {figure}. Must be 'text', 'mpl' or 'tikz'."

    inputs = self._inputs_validation(inputs)

    if figure == "mpl":
        result = qml.draw_mpl(self.circuit)(
            params=self.params,
            inputs=inputs,
            *args,
            **kwargs,
        )
    elif figure == "tikz":
        result = QuanTikz.build(
            self.circuit,
            params=self.params,
            inputs=inputs,
            *args,
            **kwargs,
        )
    else:
        result = qml.draw(self.circuit)(params=self.params, inputs=inputs)
    return result

initialize_params(rng, repeat=None, initialization=None, initialization_domain=None)

Initializes the parameters of the model.

Parameters:

Name	Type	Description	Default
rng	Generator	A random number generator to use for initialization.	required
repeat	int	The number of times to repeat the parameters. If None, the number of layers is used.	None
initialization	str	The strategy to use for parameter initialization. If None, the strategy specified in the constructor is used.	None
initialization_domain	List[float]	The domain to use for parameter initialization. If None, the domain specified in the constructor is used.	None

Returns:

Type	Description
None	None

Source code in qml_essentials/model.py

def initialize_params(
    self,
    rng: np.random.Generator,
    repeat: int = None,
    initialization: str = None,
    initialization_domain: List[float] = None,
) -> None:
    """
    Initializes the parameters of the model.

    Args:
        rng: A random number generator to use for initialization.
        repeat: The number of times to repeat the parameters.
            If None, the number of layers is used.
        initialization: The strategy to use for parameter initialization.
            If None, the strategy specified in the constructor is used.
        initialization_domain: The domain to use for parameter initialization.
            If None, the domain specified in the constructor is used.

    Returns:
        None
    """
    # Initializing params
    params_shape = (
        self._params_shape if repeat is None else [*self._params_shape, repeat]
    )
    # use existing strategy if not specified
    initialization = initialization or self._inialization_strategy
    initialization_domain = initialization_domain or self._initialization_domain

    def set_control_params(params: np.ndarray, value: float) -> np.ndarray:
        indices = self.pqc.get_control_indices(self.n_qubits)
        if indices is None:
            warnings.warn(
                f"Specified {initialization} but circuit\
                does not contain controlled rotation gates.\
                Parameters are intialized randomly.",
                UserWarning,
            )
        else:
            params[:, indices[0] : indices[1] : indices[2]] = (
                np.ones_like(params[:, indices[0] : indices[1] : indices[2]])
                * value
            )
        return params

    if initialization == "random":
        self.params: np.ndarray = rng.uniform(
            *initialization_domain, params_shape, requires_grad=True
        )
    elif initialization == "zeros":
        self.params: np.ndarray = np.zeros(params_shape, requires_grad=True)
    elif initialization == "pi":
        self.params: np.ndarray = np.ones(params_shape, requires_grad=True) * np.pi
    elif initialization == "zero-controlled":
        self.params: np.ndarray = rng.uniform(
            *initialization_domain, params_shape, requires_grad=True
        )
        self.params = set_control_params(self.params, 0)
    elif initialization == "pi-controlled":
        self.params: np.ndarray = rng.uniform(
            *initialization_domain, params_shape, requires_grad=True
        )
        self.params = set_control_params(self.params, np.pi)
    else:
        raise Exception("Invalid initialization method")

    log.info(
        f"Initialized parameters with shape {self.params.shape}\
        using strategy {initialization}."
    )

    # Initializing pulse params
    shape = (
        self._pulse_params_shape
        if repeat is None
        else (*self._pulse_params_shape, repeat)
    )
    self.pulse_params: np.ndarray = np.ones(shape, requires_grad=False)

    log.info(f"Initialized pulse parameters with shape {self.pulse_params.shape}.")

transform_input(inputs, enc_params)

Transforms the input as in arXiv:2309.03279v2

Parameters:

Name	Type	Description	Default
inputs	ndarray	single input point of shape (1, n_input_feat)	required
idx	int	feature index	required
qubit	int	qubit on which to the encoding is being performed	required
enc_params	ndarray	encoding weight vector of shape (n_qubits)	required

Returns:

Type	Description
	np.ndarray: transformed input of shape (1,), linearly scaled by
	enc_params, ready for encoding

Source code in qml_essentials/model.py

def transform_input(self, inputs: np.ndarray, enc_params: Optional[np.ndarray]):
    """
    Transforms the input as in arXiv:2309.03279v2

    Args:
        inputs (np.ndarray): single input point of shape (1, n_input_feat)
        idx (int): feature index
        qubit (int): qubit on which to the encoding is being performed
        enc_params (np.ndarray): encoding weight vector of
            shape (n_qubits)

    Returns:
        np.ndarray: transformed input of shape (1,), linearly scaled by
        enc_params, ready for encoding
    """
    return inputs * enc_params

Entanglement

from qml_essentials.entanglement import Entanglement

Source code in qml_essentials/entanglement.py

class Entanglement:

    @staticmethod
    def meyer_wallach(
        model: Model,
        n_samples: Optional[int | None],
        seed: Optional[int],
        scale: bool = False,
        **kwargs: Any,
    ) -> float:
        """
        Calculates the entangling capacity of a given quantum circuit
        using Meyer-Wallach measure.

        Args:
            model (Model): The quantum circuit model.
            n_samples (Optional[int]): Number of samples per qubit.
                If None or < 0, the current parameters of the model are used.
            seed (Optional[int]): Seed for the random number generator.
            scale (bool): Whether to scale the number of samples.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            float: Entangling capacity of the given circuit, guaranteed
                to be between 0.0 and 1.0.
        """
        if "noise_params" in kwargs:
            log.warning(
                "Meyer-Wallach measure not suitable for noisy circuits.\
                    Consider 'relative_entropy' instead."
            )

        if scale:
            n_samples = np.power(2, model.n_qubits) * n_samples

        rng = np.random.default_rng(seed)
        if n_samples is not None and n_samples > 0:
            assert seed is not None, "Seed must be provided when samples > 0"
            # TODO: maybe switch to JAX rng
            model.initialize_params(rng=rng, repeat=n_samples)
        else:
            if seed is not None:
                log.warning("Seed is ignored when samples is 0")

        # implicitly set input to none in case it's not needed
        kwargs.setdefault("inputs", None)
        # explicitly set execution type because everything else won't work
        rhos = model(execution_type="density", **kwargs).reshape(
            -1, 2**model.n_qubits, 2**model.n_qubits
        )

        measure = np.zeros(len(rhos))

        for i, rho in enumerate(rhos):
            measure[i] = Entanglement._compute_meyer_wallach_meas(rho, model.n_qubits)

        # Average all iterated states
        entangling_capability = min(max(measure.mean(), 0.0), 1.0)
        log.debug(f"Variance of measure: {measure.var()}")

        # catch floating point errors
        return float(entangling_capability)

    @staticmethod
    def _compute_meyer_wallach_meas(rho: np.ndarray, n_qubits: int):
        qb = list(range(n_qubits))
        entropy = 0
        for j in range(n_qubits):
            # Formula 6 in https://doi.org/10.48550/arXiv.quant-ph/0305094
            density = qml.math.partial_trace(rho, qb[:j] + qb[j + 1 :])
            # only real values, because imaginary part will be separate
            # in all following calculations anyway
            # entropy should be 1/2 <= entropy <= 1
            entropy += np.trace((density @ density).real)

        # inverse averaged entropy and scale to [0, 1]
        return 2 * (1 - entropy / n_qubits)

    @staticmethod
    def bell_measurements(
        model: Model, n_samples: int, seed: int, scale: bool = False, **kwargs: Any
    ) -> float:
        """
        Compute the Bell measurement for a given model.

        Args:
            model (Model): The quantum circuit model.
            n_samples (int): The number of samples to compute the measure for.
            seed (int): The seed for the random number generator.
            scale (bool): Whether to scale the number of samples
                according to the number of qubits.
            **kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            float: The Bell measurement value.
        """
        if "noise_params" in kwargs:
            log.warning(
                "Bell Measurements not suitable for noisy circuits.\
                    Consider 'relative_entropy' instead."
            )

        if scale:
            n_samples = np.power(2, model.n_qubits) * n_samples

        def _circuit(
            params: np.ndarray,
            inputs: np.ndarray,
            pulse_params: Optional[np.ndarray] = None,
            enc_params: Optional[np.ndarray] = None,
            gate_mode: str = "unitary",
        ) -> List[np.ndarray]:
            """
            Compute the Bell measurement circuit.

            Args:
                params (np.ndarray): The model parameters.
                inputs (np.ndarray): The input to the model.
                pulse_params (np.ndarray): The model pulse parameters.
                enc_params (Optional[np.ndarray]): The frequency encoding parameters.

            Returns:
                List[np.ndarray]: The probabilities of the Bell measurement.
            """
            model._variational(params, inputs, pulse_params, enc_params, gate_mode)

            qml.map_wires(
                model._variational,
                {i: i + model.n_qubits for i in range(model.n_qubits)},
            )(params, inputs)

            for q in range(model.n_qubits):
                qml.CNOT(wires=[q, q + model.n_qubits])
                qml.H(q)

            obs_wires = [(q, q + model.n_qubits) for q in range(model.n_qubits)]
            return [qml.probs(wires=w) for w in obs_wires]

        model.circuit = qml.QNode(
            _circuit,
            qml.device(
                "default.qubit",
                shots=model.shots,
                wires=model.n_qubits * 2,
            ),
        )

        rng = np.random.default_rng(seed)
        if n_samples is not None and n_samples > 0:
            assert seed is not None, "Seed must be provided when samples > 0"
            # TODO: maybe switch to JAX rng
            model.initialize_params(rng=rng, repeat=n_samples)
            params = model.params
        else:
            if seed is not None:
                log.warning("Seed is ignored when samples is 0")

            if len(model.params.shape) <= 2:
                params = model.params.reshape(*model.params.shape, 1)
            else:
                log.info(f"Using sample size of model params: {model.params.shape[-1]}")
                params = model.params

        n_samples = params.shape[-1]
        measure = np.zeros(n_samples)

        # implicitly set input to none in case it's not needed
        kwargs.setdefault("inputs", None)
        exp = model(params=params, **kwargs)
        exp = 1 - 2 * exp[..., -1]
        measure = 2 * (1 - exp.mean(axis=0))
        entangling_capability = min(max(measure.mean(), 0.0), 1.0)
        log.debug(f"Variance of measure: {measure.var()}")

        return float(entangling_capability)

    @staticmethod
    def relative_entropy(
        model: Model,
        n_samples: int,
        n_sigmas: int,
        seed: Optional[int],
        scale: bool = False,
        **kwargs: Any,
    ) -> float:
        """
        Calculates the relative entropy of entanglement of a given quantum
        circuit. This measure is also applicable to mixed state, albeit it
        might me not fully accurate in this simplified case.

        As the relative entropy is generally defined as the smallest relative
        entropy from the state in question to the set of separable states.
        However, as computing the nearest separable state is NP-hard, we select
        n_sigmas of random separable states to compute the distance to, which
        is not necessarily the nearest. Thus, this measure of entanglement
        presents an upper limit of entanglement.

        As the relative entropy is not necessarily between zero and one, this
        function also normalises by the relative entroy to the GHZ state.

        Args:
            model (Model): The quantum circuit model.
            n_samples (int): Number of samples per qubit.
                If <= 0, the current parameters of the model are used.
            n_sigmas (int): Number of random separable pure states to compare against.
            seed (Optional[int]): Seed for the random number generator.
            scale (bool): Whether to scale the number of samples.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            float: Entangling capacity of the given circuit, guaranteed
                to be between 0.0 and 1.0.
        """
        dim = np.power(2, model.n_qubits)
        if scale:
            n_samples = dim * n_samples
            n_sigmas = dim * n_sigmas

        rng = np.random.default_rng(seed)

        # Random separable states
        log_sigmas = sample_random_separable_states(
            model.n_qubits, n_samples=n_sigmas, rng=rng, take_log=True
        )

        if n_samples is not None and n_samples > 0:
            assert seed is not None, "Seed must be provided when samples > 0"
            model.initialize_params(rng=rng, repeat=n_samples)
        else:
            if seed is not None:
                log.warning("Seed is ignored when samples is 0")

            if len(model.params.shape) <= 2:
                model.params = model.params.reshape(*model.params.shape, 1)
            else:
                log.info(f"Using sample size of model params: {model.params.shape[-1]}")

        ghz_model = Model(model.n_qubits, 1, "GHZ", data_reupload=False)

        normalised_entropies = np.zeros((n_sigmas, model.params.shape[-1]))
        for j, log_sigma in enumerate(log_sigmas):

            # Entropy of GHZ states should be maximal
            ghz_entropy = Entanglement._compute_rel_entropies(
                ghz_model,
                log_sigma,
            )

            rel_entropy = Entanglement._compute_rel_entropies(
                model, log_sigma, **kwargs
            )

            normalised_entropies[j] = rel_entropy / ghz_entropy

        # Average all iterated states
        entangling_capability = normalised_entropies.min(axis=0).mean()
        log.debug(f"Variance of measure: {normalised_entropies.var()}")

        return entangling_capability

    @staticmethod
    def _compute_rel_entropies(
        model: Model,
        log_sigma: np.ndarray,
        **kwargs,
    ) -> np.ndarray:
        """
        Compute the relative entropy for a given model.

        Args:
            model (Model): The model for which to compute entanglement
            log_sigma (np.ndarray): Density matrix of next separable state

        Returns:
            np.ndarray: Relative Entropy for each sample
        """
        # implicitly set input to none in case it's not needed
        kwargs.setdefault("inputs", None)
        # explicitly set execution type because everything else won't work
        rho = model(execution_type="density", **kwargs)
        rho = rho.reshape(-1, 2**model.n_qubits, 2**model.n_qubits)
        log_rho = logm_v(rho) / np.log(2)

        rel_entropies = np.abs(np.trace(rho @ (log_rho - log_sigma), axis1=1, axis2=2))

        return rel_entropies

    @staticmethod
    def entanglement_of_formation(
        model: Model,
        n_samples: int,
        seed: Optional[int],
        scale: bool = False,
        always_decompose: bool = False,
        **kwargs: Any,
    ) -> float:
        """
        This function implements the entanglement of formation for mixed
        quantum systems.
        In that a mixed state gets decomposed into pure states with respective
        probabilities using the eigendecomposition of the density matrix.
        Then, the Meyer-Wallach measure is computed for each pure state,
        weighted by the eigenvalue.
        See e.g. https://doi.org/10.48550/arXiv.quant-ph/0504163

        Note that the decomposition is *not unique*! Therefore, this measure
        presents the entanglement for *some* decomposition into pure states,
        not necessarily the one that is anticipated when applying the Kraus
        channels.
        If a pure state is provided, this results in the same value as the
        Entanglement.meyer_wallach function if `always_decompose` flag is not set.

        Args:
            model (Model): The quantum circuit model.
            n_samples (int): Number of samples per qubit.
            seed (Optional[int]): Seed for the random number generator.
            scale (bool): Whether to scale the number of samples.
            always_decompose (bool): Whether to explicitly compute the
                entantlement of formation for the eigendecomposition of a pure
                state.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            float: Entangling capacity of the given circuit, guaranteed
                to be between 0.0 and 1.0.
        """

        if scale:
            n_samples = np.power(2, model.n_qubits) * n_samples

        rng = np.random.default_rng(seed)
        if n_samples is not None and n_samples > 0:
            assert seed is not None, "Seed must be provided when samples > 0"
            model.initialize_params(rng=rng, repeat=n_samples)
        else:
            if seed is not None:
                log.warning("Seed is ignored when samples is 0")

            if len(model.params.shape) <= 2:
                model.params = model.params.reshape(*model.params.shape, 1)
            else:
                log.info(f"Using sample size of model params: {model.params.shape[-1]}")

        # implicitly set input to none in case it's not needed
        kwargs.setdefault("inputs", None)
        rhos = model(execution_type="density", **kwargs)
        rhos = rhos.reshape(-1, 2**model.n_qubits, 2**model.n_qubits)
        entanglement = np.zeros(len(rhos))
        for i, rho in enumerate(rhos):
            entanglement[i] = Entanglement._compute_entanglement_of_formation(
                rho, model.n_qubits, always_decompose
            )
        entangling_capability = min(max(entanglement.mean(), 0.0), 1.0)
        return float(entangling_capability)

    @staticmethod
    def _compute_entanglement_of_formation(
        rho: np.ndarray, n_qubits: int, always_decompose: bool
    ) -> float:
        """
        Computes the entanglement of formation for a given density matrix rho.

        Args:
            rho (np.ndarray): The density matrix
            n_qubits (int): Number of qubits
            always_decompose (bool): Whether to explicitly compute the
                entantlement of formation for the eigendecomposition of a pure
                state.

        Returns:
            float: Entanglement for the provided state.
        """
        eigenvalues, eigenvectors = np.linalg.eigh(rho)
        if any(np.isclose(eigenvalues, 1.0)) and not always_decompose:  # Pure state
            return Entanglement._compute_meyer_wallach_meas(rho, n_qubits)
        ent = 0
        for prob, ev in zip(eigenvalues, eigenvectors):
            ev = ev.reshape(-1, 1)
            rho = ev @ np.conjugate(ev).T
            measure = Entanglement._compute_meyer_wallach_meas(rho, n_qubits)
            ent += prob * measure
        return ent

    @staticmethod
    def concentratable_entanglement(
        model: Model, n_samples: int, seed: int, scale: bool = False, **kwargs: Any
    ) -> float:
        """
        Computes the concentratable entanglement of a given model.

        This method utilizes the Concentratable Entanglement measure from
        https://arxiv.org/abs/2104.06923.

        Args:
            model (Model): The quantum circuit model.
            n_samples (int): The number of samples to compute the measure for.
            seed (int): The seed for the random number generator.
            scale (bool): Whether to scale the number of samples according to
                the number of qubits.
            **kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            float: Entangling capability of the given circuit, guaranteed
                to be between 0.0 and 1.0.
        """
        if "noise_params" in kwargs:
            log.warning(
                "Concentratable entanglement is not suitable for noisy circuits.\
                    Consider 'relative_entropy' instead."
            )

        n = model.n_qubits

        if scale:
            n_samples = np.power(2, model.n) * n_samples

        def _circuit(
            params: np.ndarray,
            inputs: np.ndarray,
            pulse_params: Optional[np.ndarray] = None,
            enc_params: Optional[np.ndarray] = None,
            gate_mode: str = "unitary",
        ) -> List[np.ndarray]:
            """
            Constructs a circuit to compute the concentratable entanglement using the
            swap test by creating two copies of the models circuit and map the output
            wires accordingly

            Args:
                params (np.ndarray): The model parameters.
                inputs (np.ndarray): The input data for the model.
                pulse_params (np.ndarray): The model pulse parameters.
                enc_params (Optional[np.ndarray]): Optional encoding parameters.

            Returns:
                List[np.ndarray]: Probabilities obtained from the swap test circuit.
            """

            qml.map_wires(model._variational, {i: i + n for i in range(n)})(
                params, inputs, pulse_params, enc_params, gate_mode
            )
            qml.map_wires(model._variational, {i: i + 2 * n for i in range(n)})(
                params, inputs, pulse_params, enc_params, gate_mode
            )

            # Perform swap test
            for i in range(n):
                qml.H(i)

            for i in range(n):
                qml.CSWAP([i, i + n, i + 2 * n])

            for i in range(n):
                qml.H(i)

            return qml.probs(wires=[i for i in range(n)])

        model.circuit = qml.QNode(
            _circuit,
            qml.device(
                "default.qubit",
                shots=model.shots,
                wires=n * 3,
            ),
        )

        rng = np.random.default_rng(seed)
        if n_samples is not None and n_samples > 0:
            assert seed is not None, "Seed must be provided when samples > 0"
            model.initialize_params(rng=rng, repeat=n_samples)
            params = model.params
        else:
            if seed is not None:
                log.warning("Seed is ignored when samples is 0")

            if len(model.params.shape) <= 2:
                params = model.params.reshape(*model.params.shape, 1)
            else:
                log.info(f"Using sample size of model params: {model.params.shape[-1]}")
                params = model.params

        n_samples = params.shape[-1]

        # implicitly set input to none in case it's not needed
        kwargs.setdefault("inputs", None)

        samples_probs = model(params=params, execution_type="probs", **kwargs)
        if n_samples == 1:
            samples_probs = [samples_probs]

        ce_measure = np.zeros(len(samples_probs))

        for i, probs in enumerate(samples_probs):
            ce_measure[i] = 1 - probs[0]

        # Average all iterated states
        entangling_capability = min(max(ce_measure.mean(), 0.0), 1.0)
        log.debug(f"Variance of measure: {ce_measure.var()}")

        # catch floating point errors
        return float(entangling_capability)

bell_measurements(model, n_samples, seed, scale=False, **kwargs) staticmethod

Compute the Bell measurement for a given model.

Parameters:

Name	Type	Description	Default
model	Model	The quantum circuit model.	required
n_samples	int	The number of samples to compute the measure for.	required
seed	int	The seed for the random number generator.	required
scale	bool	Whether to scale the number of samples according to the number of qubits.	False
**kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Name	Type	Description
float	float	The Bell measurement value.

Source code in qml_essentials/entanglement.py

@staticmethod
def bell_measurements(
    model: Model, n_samples: int, seed: int, scale: bool = False, **kwargs: Any
) -> float:
    """
    Compute the Bell measurement for a given model.

    Args:
        model (Model): The quantum circuit model.
        n_samples (int): The number of samples to compute the measure for.
        seed (int): The seed for the random number generator.
        scale (bool): Whether to scale the number of samples
            according to the number of qubits.
        **kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        float: The Bell measurement value.
    """
    if "noise_params" in kwargs:
        log.warning(
            "Bell Measurements not suitable for noisy circuits.\
                Consider 'relative_entropy' instead."
        )

    if scale:
        n_samples = np.power(2, model.n_qubits) * n_samples

    def _circuit(
        params: np.ndarray,
        inputs: np.ndarray,
        pulse_params: Optional[np.ndarray] = None,
        enc_params: Optional[np.ndarray] = None,
        gate_mode: str = "unitary",
    ) -> List[np.ndarray]:
        """
        Compute the Bell measurement circuit.

        Args:
            params (np.ndarray): The model parameters.
            inputs (np.ndarray): The input to the model.
            pulse_params (np.ndarray): The model pulse parameters.
            enc_params (Optional[np.ndarray]): The frequency encoding parameters.

        Returns:
            List[np.ndarray]: The probabilities of the Bell measurement.
        """
        model._variational(params, inputs, pulse_params, enc_params, gate_mode)

        qml.map_wires(
            model._variational,
            {i: i + model.n_qubits for i in range(model.n_qubits)},
        )(params, inputs)

        for q in range(model.n_qubits):
            qml.CNOT(wires=[q, q + model.n_qubits])
            qml.H(q)

        obs_wires = [(q, q + model.n_qubits) for q in range(model.n_qubits)]
        return [qml.probs(wires=w) for w in obs_wires]

    model.circuit = qml.QNode(
        _circuit,
        qml.device(
            "default.qubit",
            shots=model.shots,
            wires=model.n_qubits * 2,
        ),
    )

    rng = np.random.default_rng(seed)
    if n_samples is not None and n_samples > 0:
        assert seed is not None, "Seed must be provided when samples > 0"
        # TODO: maybe switch to JAX rng
        model.initialize_params(rng=rng, repeat=n_samples)
        params = model.params
    else:
        if seed is not None:
            log.warning("Seed is ignored when samples is 0")

        if len(model.params.shape) <= 2:
            params = model.params.reshape(*model.params.shape, 1)
        else:
            log.info(f"Using sample size of model params: {model.params.shape[-1]}")
            params = model.params

    n_samples = params.shape[-1]
    measure = np.zeros(n_samples)

    # implicitly set input to none in case it's not needed
    kwargs.setdefault("inputs", None)
    exp = model(params=params, **kwargs)
    exp = 1 - 2 * exp[..., -1]
    measure = 2 * (1 - exp.mean(axis=0))
    entangling_capability = min(max(measure.mean(), 0.0), 1.0)
    log.debug(f"Variance of measure: {measure.var()}")

    return float(entangling_capability)

concentratable_entanglement(model, n_samples, seed, scale=False, **kwargs) staticmethod

Computes the concentratable entanglement of a given model.

This method utilizes the Concentratable Entanglement measure from https://arxiv.org/abs/2104.06923.

Parameters:

Name	Type	Description	Default
model	Model	The quantum circuit model.	required
n_samples	int	The number of samples to compute the measure for.	required
seed	int	The seed for the random number generator.	required
scale	bool	Whether to scale the number of samples according to the number of qubits.	False
**kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Name	Type	Description
float	float	Entangling capability of the given circuit, guaranteed to be between 0.0 and 1.0.

Source code in qml_essentials/entanglement.py

@staticmethod
def concentratable_entanglement(
    model: Model, n_samples: int, seed: int, scale: bool = False, **kwargs: Any
) -> float:
    """
    Computes the concentratable entanglement of a given model.

    This method utilizes the Concentratable Entanglement measure from
    https://arxiv.org/abs/2104.06923.

    Args:
        model (Model): The quantum circuit model.
        n_samples (int): The number of samples to compute the measure for.
        seed (int): The seed for the random number generator.
        scale (bool): Whether to scale the number of samples according to
            the number of qubits.
        **kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        float: Entangling capability of the given circuit, guaranteed
            to be between 0.0 and 1.0.
    """
    if "noise_params" in kwargs:
        log.warning(
            "Concentratable entanglement is not suitable for noisy circuits.\
                Consider 'relative_entropy' instead."
        )

    n = model.n_qubits

    if scale:
        n_samples = np.power(2, model.n) * n_samples

    def _circuit(
        params: np.ndarray,
        inputs: np.ndarray,
        pulse_params: Optional[np.ndarray] = None,
        enc_params: Optional[np.ndarray] = None,
        gate_mode: str = "unitary",
    ) -> List[np.ndarray]:
        """
        Constructs a circuit to compute the concentratable entanglement using the
        swap test by creating two copies of the models circuit and map the output
        wires accordingly

        Args:
            params (np.ndarray): The model parameters.
            inputs (np.ndarray): The input data for the model.
            pulse_params (np.ndarray): The model pulse parameters.
            enc_params (Optional[np.ndarray]): Optional encoding parameters.

        Returns:
            List[np.ndarray]: Probabilities obtained from the swap test circuit.
        """

        qml.map_wires(model._variational, {i: i + n for i in range(n)})(
            params, inputs, pulse_params, enc_params, gate_mode
        )
        qml.map_wires(model._variational, {i: i + 2 * n for i in range(n)})(
            params, inputs, pulse_params, enc_params, gate_mode
        )

        # Perform swap test
        for i in range(n):
            qml.H(i)

        for i in range(n):
            qml.CSWAP([i, i + n, i + 2 * n])

        for i in range(n):
            qml.H(i)

        return qml.probs(wires=[i for i in range(n)])

    model.circuit = qml.QNode(
        _circuit,
        qml.device(
            "default.qubit",
            shots=model.shots,
            wires=n * 3,
        ),
    )

    rng = np.random.default_rng(seed)
    if n_samples is not None and n_samples > 0:
        assert seed is not None, "Seed must be provided when samples > 0"
        model.initialize_params(rng=rng, repeat=n_samples)
        params = model.params
    else:
        if seed is not None:
            log.warning("Seed is ignored when samples is 0")

        if len(model.params.shape) <= 2:
            params = model.params.reshape(*model.params.shape, 1)
        else:
            log.info(f"Using sample size of model params: {model.params.shape[-1]}")
            params = model.params

    n_samples = params.shape[-1]

    # implicitly set input to none in case it's not needed
    kwargs.setdefault("inputs", None)

    samples_probs = model(params=params, execution_type="probs", **kwargs)
    if n_samples == 1:
        samples_probs = [samples_probs]

    ce_measure = np.zeros(len(samples_probs))

    for i, probs in enumerate(samples_probs):
        ce_measure[i] = 1 - probs[0]

    # Average all iterated states
    entangling_capability = min(max(ce_measure.mean(), 0.0), 1.0)
    log.debug(f"Variance of measure: {ce_measure.var()}")

    # catch floating point errors
    return float(entangling_capability)

entanglement_of_formation(model, n_samples, seed, scale=False, always_decompose=False, **kwargs) staticmethod

This function implements the entanglement of formation for mixed quantum systems. In that a mixed state gets decomposed into pure states with respective probabilities using the eigendecomposition of the density matrix. Then, the Meyer-Wallach measure is computed for each pure state, weighted by the eigenvalue. See e.g. https://doi.org/10.48550/arXiv.quant-ph/0504163

Note that the decomposition is not unique! Therefore, this measure presents the entanglement for some decomposition into pure states, not necessarily the one that is anticipated when applying the Kraus channels. If a pure state is provided, this results in the same value as the Entanglement.meyer_wallach function if always_decompose flag is not set.

Parameters:

Name	Type	Description	Default
model	Model	The quantum circuit model.	required
n_samples	int	Number of samples per qubit.	required
seed	Optional[int]	Seed for the random number generator.	required
scale	bool	Whether to scale the number of samples.	False
always_decompose	bool	Whether to explicitly compute the entantlement of formation for the eigendecomposition of a pure state.	False
kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Name	Type	Description
float	float	Entangling capacity of the given circuit, guaranteed to be between 0.0 and 1.0.

Source code in qml_essentials/entanglement.py

@staticmethod
def entanglement_of_formation(
    model: Model,
    n_samples: int,
    seed: Optional[int],
    scale: bool = False,
    always_decompose: bool = False,
    **kwargs: Any,
) -> float:
    """
    This function implements the entanglement of formation for mixed
    quantum systems.
    In that a mixed state gets decomposed into pure states with respective
    probabilities using the eigendecomposition of the density matrix.
    Then, the Meyer-Wallach measure is computed for each pure state,
    weighted by the eigenvalue.
    See e.g. https://doi.org/10.48550/arXiv.quant-ph/0504163

    Note that the decomposition is *not unique*! Therefore, this measure
    presents the entanglement for *some* decomposition into pure states,
    not necessarily the one that is anticipated when applying the Kraus
    channels.
    If a pure state is provided, this results in the same value as the
    Entanglement.meyer_wallach function if `always_decompose` flag is not set.

    Args:
        model (Model): The quantum circuit model.
        n_samples (int): Number of samples per qubit.
        seed (Optional[int]): Seed for the random number generator.
        scale (bool): Whether to scale the number of samples.
        always_decompose (bool): Whether to explicitly compute the
            entantlement of formation for the eigendecomposition of a pure
            state.
        kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        float: Entangling capacity of the given circuit, guaranteed
            to be between 0.0 and 1.0.
    """

    if scale:
        n_samples = np.power(2, model.n_qubits) * n_samples

    rng = np.random.default_rng(seed)
    if n_samples is not None and n_samples > 0:
        assert seed is not None, "Seed must be provided when samples > 0"
        model.initialize_params(rng=rng, repeat=n_samples)
    else:
        if seed is not None:
            log.warning("Seed is ignored when samples is 0")

        if len(model.params.shape) <= 2:
            model.params = model.params.reshape(*model.params.shape, 1)
        else:
            log.info(f"Using sample size of model params: {model.params.shape[-1]}")

    # implicitly set input to none in case it's not needed
    kwargs.setdefault("inputs", None)
    rhos = model(execution_type="density", **kwargs)
    rhos = rhos.reshape(-1, 2**model.n_qubits, 2**model.n_qubits)
    entanglement = np.zeros(len(rhos))
    for i, rho in enumerate(rhos):
        entanglement[i] = Entanglement._compute_entanglement_of_formation(
            rho, model.n_qubits, always_decompose
        )
    entangling_capability = min(max(entanglement.mean(), 0.0), 1.0)
    return float(entangling_capability)

meyer_wallach(model, n_samples, seed, scale=False, **kwargs) staticmethod

Calculates the entangling capacity of a given quantum circuit using Meyer-Wallach measure.

Parameters:

Name	Type	Description	Default
model	Model	The quantum circuit model.	required
n_samples	Optional[int]	Number of samples per qubit. If None or < 0, the current parameters of the model are used.	required
seed	Optional[int]	Seed for the random number generator.	required
scale	bool	Whether to scale the number of samples.	False
kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Name	Type	Description
float	float	Entangling capacity of the given circuit, guaranteed to be between 0.0 and 1.0.

Source code in qml_essentials/entanglement.py

@staticmethod
def meyer_wallach(
    model: Model,
    n_samples: Optional[int | None],
    seed: Optional[int],
    scale: bool = False,
    **kwargs: Any,
) -> float:
    """
    Calculates the entangling capacity of a given quantum circuit
    using Meyer-Wallach measure.

    Args:
        model (Model): The quantum circuit model.
        n_samples (Optional[int]): Number of samples per qubit.
            If None or < 0, the current parameters of the model are used.
        seed (Optional[int]): Seed for the random number generator.
        scale (bool): Whether to scale the number of samples.
        kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        float: Entangling capacity of the given circuit, guaranteed
            to be between 0.0 and 1.0.
    """
    if "noise_params" in kwargs:
        log.warning(
            "Meyer-Wallach measure not suitable for noisy circuits.\
                Consider 'relative_entropy' instead."
        )

    if scale:
        n_samples = np.power(2, model.n_qubits) * n_samples

    rng = np.random.default_rng(seed)
    if n_samples is not None and n_samples > 0:
        assert seed is not None, "Seed must be provided when samples > 0"
        # TODO: maybe switch to JAX rng
        model.initialize_params(rng=rng, repeat=n_samples)
    else:
        if seed is not None:
            log.warning("Seed is ignored when samples is 0")

    # implicitly set input to none in case it's not needed
    kwargs.setdefault("inputs", None)
    # explicitly set execution type because everything else won't work
    rhos = model(execution_type="density", **kwargs).reshape(
        -1, 2**model.n_qubits, 2**model.n_qubits
    )

    measure = np.zeros(len(rhos))

    for i, rho in enumerate(rhos):
        measure[i] = Entanglement._compute_meyer_wallach_meas(rho, model.n_qubits)

    # Average all iterated states
    entangling_capability = min(max(measure.mean(), 0.0), 1.0)
    log.debug(f"Variance of measure: {measure.var()}")

    # catch floating point errors
    return float(entangling_capability)

relative_entropy(model, n_samples, n_sigmas, seed, scale=False, **kwargs) staticmethod

Calculates the relative entropy of entanglement of a given quantum circuit. This measure is also applicable to mixed state, albeit it might me not fully accurate in this simplified case.

As the relative entropy is generally defined as the smallest relative entropy from the state in question to the set of separable states. However, as computing the nearest separable state is NP-hard, we select n_sigmas of random separable states to compute the distance to, which is not necessarily the nearest. Thus, this measure of entanglement presents an upper limit of entanglement.

As the relative entropy is not necessarily between zero and one, this function also normalises by the relative entroy to the GHZ state.

Parameters:

Name	Type	Description	Default
model	Model	The quantum circuit model.	required
n_samples	int	Number of samples per qubit. If <= 0, the current parameters of the model are used.	required
n_sigmas	int	Number of random separable pure states to compare against.	required
seed	Optional[int]	Seed for the random number generator.	required
scale	bool	Whether to scale the number of samples.	False
kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Name	Type	Description
float	float	Entangling capacity of the given circuit, guaranteed to be between 0.0 and 1.0.

Source code in qml_essentials/entanglement.py

@staticmethod
def relative_entropy(
    model: Model,
    n_samples: int,
    n_sigmas: int,
    seed: Optional[int],
    scale: bool = False,
    **kwargs: Any,
) -> float:
    """
    Calculates the relative entropy of entanglement of a given quantum
    circuit. This measure is also applicable to mixed state, albeit it
    might me not fully accurate in this simplified case.

    As the relative entropy is generally defined as the smallest relative
    entropy from the state in question to the set of separable states.
    However, as computing the nearest separable state is NP-hard, we select
    n_sigmas of random separable states to compute the distance to, which
    is not necessarily the nearest. Thus, this measure of entanglement
    presents an upper limit of entanglement.

    As the relative entropy is not necessarily between zero and one, this
    function also normalises by the relative entroy to the GHZ state.

    Args:
        model (Model): The quantum circuit model.
        n_samples (int): Number of samples per qubit.
            If <= 0, the current parameters of the model are used.
        n_sigmas (int): Number of random separable pure states to compare against.
        seed (Optional[int]): Seed for the random number generator.
        scale (bool): Whether to scale the number of samples.
        kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        float: Entangling capacity of the given circuit, guaranteed
            to be between 0.0 and 1.0.
    """
    dim = np.power(2, model.n_qubits)
    if scale:
        n_samples = dim * n_samples
        n_sigmas = dim * n_sigmas

    rng = np.random.default_rng(seed)

    # Random separable states
    log_sigmas = sample_random_separable_states(
        model.n_qubits, n_samples=n_sigmas, rng=rng, take_log=True
    )

    if n_samples is not None and n_samples > 0:
        assert seed is not None, "Seed must be provided when samples > 0"
        model.initialize_params(rng=rng, repeat=n_samples)
    else:
        if seed is not None:
            log.warning("Seed is ignored when samples is 0")

        if len(model.params.shape) <= 2:
            model.params = model.params.reshape(*model.params.shape, 1)
        else:
            log.info(f"Using sample size of model params: {model.params.shape[-1]}")

    ghz_model = Model(model.n_qubits, 1, "GHZ", data_reupload=False)

    normalised_entropies = np.zeros((n_sigmas, model.params.shape[-1]))
    for j, log_sigma in enumerate(log_sigmas):

        # Entropy of GHZ states should be maximal
        ghz_entropy = Entanglement._compute_rel_entropies(
            ghz_model,
            log_sigma,
        )

        rel_entropy = Entanglement._compute_rel_entropies(
            model, log_sigma, **kwargs
        )

        normalised_entropies[j] = rel_entropy / ghz_entropy

    # Average all iterated states
    entangling_capability = normalised_entropies.min(axis=0).mean()
    log.debug(f"Variance of measure: {normalised_entropies.var()}")

    return entangling_capability

Expressibility

from qml_essentials.expressibility import Expressibility

Source code in qml_essentials/expressibility.py

class Expressibility:
    @staticmethod
    def _sample_state_fidelities(
        model: Model,
        x_samples: np.ndarray,
        n_samples: int,
        seed: int,
        kwargs: Any,
    ) -> np.ndarray:
        """
        Compute the fidelities for each pair of input samples and parameter sets.

        Args:
            model (Callable): Function that models the quantum circuit.
            x_samples (np.ndarray): Array of shape (n_input_samples, n_features)
                containing the input samples.
            n_samples (int): Number of parameter sets to generate.
            seed (int): Random number generator seed.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            np.ndarray: Array of shape (n_input_samples, n_samples)
            containing the fidelities.
        """
        rng = np.random.default_rng(seed)

        # Generate random parameter sets
        # We need two sets of parameters, as we are computing fidelities for a
        # pair of random state vectors
        model.initialize_params(rng=rng, repeat=n_samples * 2)

        # Initialize array to store fidelities
        fidelities: np.ndarray = np.zeros((len(x_samples), n_samples))

        # Compute the fidelity for each pair of input samples and parameters
        for idx, x_sample in enumerate(x_samples):

            # Evaluate the model for the current pair of input samples and parameters
            # Execution type is explicitly set to density
            sv: np.ndarray = model(
                inputs=x_sample,
                params=model.params,
                execution_type="density",
                **kwargs,
            )

            # $\sqrt{\rho}$
            sqrt_sv1: np.ndarray = np.array([sqrtm(m) for m in sv[:n_samples]])

            # $\sqrt{\rho} \sigma \sqrt{\rho}$
            inner_fidelity = sqrt_sv1 @ sv[n_samples:] @ sqrt_sv1

            # Compute the fidelity using the partial trace of the statevector
            fidelity: np.ndarray = (
                np.trace(
                    np.array([sqrtm(m) for m in inner_fidelity]),
                    axis1=1,
                    axis2=2,
                )
                ** 2
            )

            fidelities[idx] = np.abs(fidelity)

        return fidelities

    @staticmethod
    def state_fidelities(
        seed: int,
        n_samples: int,
        n_bins: int,
        model: Model,
        n_input_samples: int = 0,
        input_domain: List[float] = None,
        scale: bool = False,
        **kwargs: Any,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Sample the state fidelities and histogram them into a 2D array.

        Args:
            seed (int): Random number generator seed.
            n_samples (int): Number of parameter sets to generate.
            n_bins (int): Number of histogram bins.
            n_input_samples (int): Number of input samples.
            input_domain (List[float]): Input domain.
            model (Callable): Function that models the quantum circuit.
            scale (bool): Whether to scale the number of samples and bins.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            Tuple[np.ndarray, np.ndarray, np.ndarray]: Tuple containing the
                input samples, bin edges, and histogram values.
        """
        if scale:
            n_samples = np.power(2, model.n_qubits) * n_samples
            n_bins = model.n_qubits * n_bins

        if input_domain is None or n_input_samples is None or n_input_samples == 0:
            x = np.zeros((1))
            n_input_samples = 1
        else:
            x = np.linspace(*input_domain, n_input_samples, requires_grad=False)

        fidelities = Expressibility._sample_state_fidelities(
            x_samples=x,
            n_samples=n_samples,
            seed=seed,
            model=model,
            kwargs=kwargs,
        )
        z: np.ndarray = np.zeros((n_input_samples, n_bins))

        y: np.ndarray = np.linspace(0, 1, n_bins + 1)

        for i, f in enumerate(fidelities):
            z[i], _ = np.histogram(f, bins=y)

        z = z / n_samples

        if z.shape[0] == 1:
            z = z.flatten()

        return x, y, z

    @staticmethod
    def _haar_probability(fidelity: float, n_qubits: int) -> float:
        """
        Calculates theoretical probability density function for random Haar states
        as proposed by Sim et al. (https://arxiv.org/abs/1905.10876).

        Args:
            fidelity (float): fidelity of two parameter assignments in [0, 1]
            n_qubits (int): number of qubits in the quantum system

        Returns:
            float: probability for a given fidelity
        """
        N = 2**n_qubits

        prob = (N - 1) * (1 - fidelity) ** (N - 2)
        return prob

    @staticmethod
    def _sample_haar_integral(n_qubits: int, n_bins: int) -> np.ndarray:
        """
        Calculates theoretical probability density function for random Haar states
        as proposed by Sim et al. (https://arxiv.org/abs/1905.10876) and bins it
        into a 2D-histogram.

        Args:
            n_qubits (int): number of qubits in the quantum system
            n_bins (int): number of histogram bins

        Returns:
            np.ndarray: probability distribution for all fidelities
        """
        dist = np.zeros(n_bins)
        for idx in range(n_bins):
            v = idx / n_bins
            u = (idx + 1) / n_bins
            dist[idx], _ = integrate.quad(
                Expressibility._haar_probability, v, u, args=(n_qubits,)
            )

        return dist

    @staticmethod
    def haar_integral(
        n_qubits: int,
        n_bins: int,
        cache: bool = True,
        scale: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Calculates theoretical probability density function for random Haar states
        as proposed by Sim et al. (https://arxiv.org/abs/1905.10876) and bins it
        into a 3D-histogram.

        Args:
            n_qubits (int): number of qubits in the quantum system
            n_bins (int): number of histogram bins
            cache (bool): whether to cache the haar integral
            scale (bool): whether to scale the number of bins

        Returns:
            Tuple[np.ndarray, np.ndarray]:
                - x component (bins): the input domain
                - y component (probabilities): the haar probability density
                  funtion for random Haar states
        """
        if scale:
            n_bins = n_qubits * n_bins

        x = np.linspace(0, 1, n_bins)

        if cache:
            name = f"haar_{n_qubits}q_{n_bins}s_{'scaled' if scale else ''}.npy"

            cache_folder = ".cache"
            if not os.path.exists(cache_folder):
                os.mkdir(cache_folder)

            file_path = os.path.join(cache_folder, name)

            if os.path.isfile(file_path):
                y = np.load(file_path)
                return x, y

        y = Expressibility._sample_haar_integral(n_qubits, n_bins)

        if cache:
            np.save(file_path, y)

        return x, y

    @staticmethod
    def kullback_leibler_divergence(
        vqc_prob_dist: np.ndarray,
        haar_dist: np.ndarray,
    ) -> np.ndarray:
        """
        Calculates the KL divergence between two probability distributions (Haar
        probability distribution and the fidelity distribution sampled from a VQC).

        Args:
            vqc_prob_dist (np.ndarray): VQC fidelity probability distribution.
                Should have shape (n_inputs_samples, n_bins)
            haar_dist (np.ndarray): Haar probability distribution with shape.
                Should have shape (n_bins, )

        Returns:
            np.ndarray: Array of KL-Divergence values for all values in axis 1
        """
        if len(vqc_prob_dist.shape) > 1:
            assert all([haar_dist.shape == p.shape for p in vqc_prob_dist]), (
                "All probabilities for inputs should have the same shape as Haar. "
                f"Got {haar_dist.shape} for Haar and {vqc_prob_dist.shape} for VQC"
            )
        else:
            vqc_prob_dist = vqc_prob_dist.reshape((1, -1))

        kl_divergence = np.zeros(vqc_prob_dist.shape[0])
        for idx, p in enumerate(vqc_prob_dist):
            kl_divergence[idx] = np.sum(rel_entr(p, haar_dist))

        return kl_divergence

haar_integral(n_qubits, n_bins, cache=True, scale=False) staticmethod

Calculates theoretical probability density function for random Haar states as proposed by Sim et al. (https://arxiv.org/abs/1905.10876) and bins it into a 3D-histogram.

Parameters:

Name	Type	Description	Default
n_qubits	int	number of qubits in the quantum system	required
n_bins	int	number of histogram bins	required
cache	bool	whether to cache the haar integral	True
scale	bool	whether to scale the number of bins	False

Returns:

Type	Description
Tuple[ndarray, ndarray]	Tuple[np.ndarray, np.ndarray]: - x component (bins): the input domain - y component (probabilities): the haar probability density funtion for random Haar states

Source code in qml_essentials/expressibility.py

@staticmethod
def haar_integral(
    n_qubits: int,
    n_bins: int,
    cache: bool = True,
    scale: bool = False,
) -> Tuple[np.ndarray, np.ndarray]:
    """
    Calculates theoretical probability density function for random Haar states
    as proposed by Sim et al. (https://arxiv.org/abs/1905.10876) and bins it
    into a 3D-histogram.

    Args:
        n_qubits (int): number of qubits in the quantum system
        n_bins (int): number of histogram bins
        cache (bool): whether to cache the haar integral
        scale (bool): whether to scale the number of bins

    Returns:
        Tuple[np.ndarray, np.ndarray]:
            - x component (bins): the input domain
            - y component (probabilities): the haar probability density
              funtion for random Haar states
    """
    if scale:
        n_bins = n_qubits * n_bins

    x = np.linspace(0, 1, n_bins)

    if cache:
        name = f"haar_{n_qubits}q_{n_bins}s_{'scaled' if scale else ''}.npy"

        cache_folder = ".cache"
        if not os.path.exists(cache_folder):
            os.mkdir(cache_folder)

        file_path = os.path.join(cache_folder, name)

        if os.path.isfile(file_path):
            y = np.load(file_path)
            return x, y

    y = Expressibility._sample_haar_integral(n_qubits, n_bins)

    if cache:
        np.save(file_path, y)

    return x, y

kullback_leibler_divergence(vqc_prob_dist, haar_dist) staticmethod

Calculates the KL divergence between two probability distributions (Haar probability distribution and the fidelity distribution sampled from a VQC).

Parameters:

Name	Type	Description	Default
vqc_prob_dist	ndarray	VQC fidelity probability distribution. Should have shape (n_inputs_samples, n_bins)	required
haar_dist	ndarray	Haar probability distribution with shape. Should have shape (n_bins, )	required

Returns:

Type	Description
ndarray	np.ndarray: Array of KL-Divergence values for all values in axis 1

Source code in qml_essentials/expressibility.py

@staticmethod
def kullback_leibler_divergence(
    vqc_prob_dist: np.ndarray,
    haar_dist: np.ndarray,
) -> np.ndarray:
    """
    Calculates the KL divergence between two probability distributions (Haar
    probability distribution and the fidelity distribution sampled from a VQC).

    Args:
        vqc_prob_dist (np.ndarray): VQC fidelity probability distribution.
            Should have shape (n_inputs_samples, n_bins)
        haar_dist (np.ndarray): Haar probability distribution with shape.
            Should have shape (n_bins, )

    Returns:
        np.ndarray: Array of KL-Divergence values for all values in axis 1
    """
    if len(vqc_prob_dist.shape) > 1:
        assert all([haar_dist.shape == p.shape for p in vqc_prob_dist]), (
            "All probabilities for inputs should have the same shape as Haar. "
            f"Got {haar_dist.shape} for Haar and {vqc_prob_dist.shape} for VQC"
        )
    else:
        vqc_prob_dist = vqc_prob_dist.reshape((1, -1))

    kl_divergence = np.zeros(vqc_prob_dist.shape[0])
    for idx, p in enumerate(vqc_prob_dist):
        kl_divergence[idx] = np.sum(rel_entr(p, haar_dist))

    return kl_divergence

state_fidelities(seed, n_samples, n_bins, model, n_input_samples=0, input_domain=None, scale=False, **kwargs) staticmethod

Sample the state fidelities and histogram them into a 2D array.

Parameters:

Name	Type	Description	Default
seed	int	Random number generator seed.	required
n_samples	int	Number of parameter sets to generate.	required
n_bins	int	Number of histogram bins.	required
n_input_samples	int	Number of input samples.	0
input_domain	List[float]	Input domain.	None
model	Callable	Function that models the quantum circuit.	required
scale	bool	Whether to scale the number of samples and bins.	False
kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Type	Description
Tuple[ndarray, ndarray, ndarray]	Tuple[np.ndarray, np.ndarray, np.ndarray]: Tuple containing the input samples, bin edges, and histogram values.

Source code in qml_essentials/expressibility.py

@staticmethod
def state_fidelities(
    seed: int,
    n_samples: int,
    n_bins: int,
    model: Model,
    n_input_samples: int = 0,
    input_domain: List[float] = None,
    scale: bool = False,
    **kwargs: Any,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Sample the state fidelities and histogram them into a 2D array.

    Args:
        seed (int): Random number generator seed.
        n_samples (int): Number of parameter sets to generate.
        n_bins (int): Number of histogram bins.
        n_input_samples (int): Number of input samples.
        input_domain (List[float]): Input domain.
        model (Callable): Function that models the quantum circuit.
        scale (bool): Whether to scale the number of samples and bins.
        kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        Tuple[np.ndarray, np.ndarray, np.ndarray]: Tuple containing the
            input samples, bin edges, and histogram values.
    """
    if scale:
        n_samples = np.power(2, model.n_qubits) * n_samples
        n_bins = model.n_qubits * n_bins

    if input_domain is None or n_input_samples is None or n_input_samples == 0:
        x = np.zeros((1))
        n_input_samples = 1
    else:
        x = np.linspace(*input_domain, n_input_samples, requires_grad=False)

    fidelities = Expressibility._sample_state_fidelities(
        x_samples=x,
        n_samples=n_samples,
        seed=seed,
        model=model,
        kwargs=kwargs,
    )
    z: np.ndarray = np.zeros((n_input_samples, n_bins))

    y: np.ndarray = np.linspace(0, 1, n_bins + 1)

    for i, f in enumerate(fidelities):
        z[i], _ = np.histogram(f, bins=y)

    z = z / n_samples

    if z.shape[0] == 1:
        z = z.flatten()

    return x, y, z

Coefficients

from qml_essentials.coefficients import Coefficients

Source code in qml_essentials/coefficients.py

class Coefficients:
    @staticmethod
    def get_spectrum(
        model: Model,
        mfs: int = 1,
        mts: int = 1,
        shift=False,
        trim=False,
        **kwargs,
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Extracts the coefficients of a given model using a FFT (np-fft).

        Note that the coefficients are complex numbers, but the imaginary part
        of the coefficients should be very close to zero, since the expectation
        values of the Pauli operators are real numbers.

        It can perform oversampling in both the frequency and time domain
        using the `mfs` and `mts` arguments.

        Args:
            model (Model): The model to sample.
            mfs (int): Multiplicator for the highest frequency. Default is 2.
            mts (int): Multiplicator for the number of time samples. Default is 1.
            shift (bool): Whether to apply np-fftshift. Default is False.
            trim (bool): Whether to remove the Nyquist frequency if spectrum is even.
                Default is False.
            kwargs (Any): Additional keyword arguments for the model function.

        Returns:
            Tuple[np.ndarray, np.ndarray]: Tuple containing the coefficients
            and frequencies.
        """
        kwargs.setdefault("force_mean", True)
        kwargs.setdefault("execution_type", "expval")

        coeffs, freqs = Coefficients._fourier_transform(
            model, mfs=mfs, mts=mts, **kwargs
        )

        if not np.isclose(np.sum(coeffs).imag, 0.0, rtol=1.0e-5):
            raise ValueError(
                f"Spectrum is not real. Imaginary part of coefficients is:\
                {np.sum(coeffs).imag}"
            )

        if trim:
            for ax in range(model.n_input_feat):
                if coeffs.shape[ax] % 2 == 0:
                    coeffs = np.delete(coeffs, len(coeffs) // 2, axis=ax)
                    freqs = np.delete(freqs, len(freqs) // 2, axis=ax)

        if shift:
            coeffs = np.fft.fftshift(coeffs, axes=list(range(model.n_input_feat)))
            freqs = np.fft.fftshift(freqs)

        return coeffs, freqs

    @staticmethod
    def _fourier_transform(
        model: Model, mfs: int, mts: int, **kwargs: Any
    ) -> np.ndarray:
        # Create a frequency vector with as many frequencies as model degrees,
        # oversampled by nfs
        n_freqs: int = 2 * mfs * model.degree + 1

        start, stop, step = 0, 2 * mts * np.pi, 2 * np.pi / n_freqs
        # Stretch according to the number of frequencies
        inputs: np.ndarray = np.arange(start, stop, step)

        # permute with input dimensionality
        nd_inputs = np.array(np.meshgrid(*[inputs] * model.n_input_feat)).T.reshape(
            -1, model.n_input_feat
        )

        # Output vector is not necessarily the same length as input
        outputs = model(inputs=nd_inputs, **kwargs)
        outputs = outputs.reshape(*(inputs.shape * model.n_input_feat), -1).squeeze()

        coeffs = np.fft.fftn(outputs, axes=list(range(model.n_input_feat)))

        # TODO: in the future, this should take into account that there can be a
        # different number of frequencies per dimension
        freqs = [
            np.fft.fftfreq(mts * n_freqs, 1 / n_freqs)
            for _ in range(model.n_input_feat)
        ]
        # freqs = np.fft.fftfreq(mts * n_freqs, 1 / n_freqs)

        # TODO: this could cause issues with multidim input
        # FIXME: account for different frequencies in multidim input scenarios
        # Run the fft and rearrange +
        # normalize the output (using product if multidim)
        return (
            coeffs / np.prod(outputs.shape[0 : model.n_input_feat]),
            np.array(freqs).squeeze(),
        )

    @staticmethod
    def get_psd(coeffs: np.ndarray) -> np.ndarray:
        """
        Calculates the power spectral density (PSD) from given Fourier coefficients.

        Args:
            coeffs (np.ndarray): The Fourier coefficients.

        Returns:
            np.ndarray: The power spectral density.
        """
        # TODO: if we apply trim=True in advance, this will be slightly wrong..

        def abs2(x):
            return x.real**2 + x.imag**2

        scale = 2.0 / (len(coeffs) ** 2)
        return scale * abs2(coeffs)

    @staticmethod
    def evaluate_Fourier_series(
        coefficients: np.ndarray,
        frequencies: np.ndarray,
        inputs: Union[np.ndarray, list, float],
    ) -> float:
        """
        Evaluate the function value of a Fourier series at one point.

        Args:
            coefficients (np.ndarray): Coefficients of the Fourier series.
            frequencies (np.ndarray): Corresponding frequencies.
            inputs (np.ndarray): Point at which to evaluate the function.
        Returns:
            float: The function value at the input point.
        """
        dims = len(coefficients.shape)

        if not isinstance(inputs, (np.ndarray, list)):
            inputs = [inputs]

        frequencies = np.stack(np.meshgrid(*frequencies)).T.reshape(-1, dims)
        freq_inputs = np.einsum("...j,j->...", frequencies, inputs)
        coeffs = coefficients.flatten()
        freq_inputs = freq_inputs.flatten()

        exp = 0.0
        for omega_x, c in zip(freq_inputs, coeffs):
            exp += c * np.exp(1j * omega_x)

        return np.real_if_close(exp)

evaluate_Fourier_series(coefficients, frequencies, inputs) staticmethod

Evaluate the function value of a Fourier series at one point.

Parameters:

Name	Type	Description	Default
coefficients	ndarray	Coefficients of the Fourier series.	required
frequencies	ndarray	Corresponding frequencies.	required
inputs	ndarray	Point at which to evaluate the function.	required

Returns: float: The function value at the input point.

Source code in qml_essentials/coefficients.py

@staticmethod
def evaluate_Fourier_series(
    coefficients: np.ndarray,
    frequencies: np.ndarray,
    inputs: Union[np.ndarray, list, float],
) -> float:
    """
    Evaluate the function value of a Fourier series at one point.

    Args:
        coefficients (np.ndarray): Coefficients of the Fourier series.
        frequencies (np.ndarray): Corresponding frequencies.
        inputs (np.ndarray): Point at which to evaluate the function.
    Returns:
        float: The function value at the input point.
    """
    dims = len(coefficients.shape)

    if not isinstance(inputs, (np.ndarray, list)):
        inputs = [inputs]

    frequencies = np.stack(np.meshgrid(*frequencies)).T.reshape(-1, dims)
    freq_inputs = np.einsum("...j,j->...", frequencies, inputs)
    coeffs = coefficients.flatten()
    freq_inputs = freq_inputs.flatten()

    exp = 0.0
    for omega_x, c in zip(freq_inputs, coeffs):
        exp += c * np.exp(1j * omega_x)

    return np.real_if_close(exp)

get_psd(coeffs) staticmethod

Calculates the power spectral density (PSD) from given Fourier coefficients.

Parameters:

Name	Type	Description	Default
coeffs	ndarray	The Fourier coefficients.	required

Returns:

Type	Description
ndarray	np.ndarray: The power spectral density.

Source code in qml_essentials/coefficients.py

@staticmethod
def get_psd(coeffs: np.ndarray) -> np.ndarray:
    """
    Calculates the power spectral density (PSD) from given Fourier coefficients.

    Args:
        coeffs (np.ndarray): The Fourier coefficients.

    Returns:
        np.ndarray: The power spectral density.
    """
    # TODO: if we apply trim=True in advance, this will be slightly wrong..

    def abs2(x):
        return x.real**2 + x.imag**2

    scale = 2.0 / (len(coeffs) ** 2)
    return scale * abs2(coeffs)

get_spectrum(model, mfs=1, mts=1, shift=False, trim=False, **kwargs) staticmethod

Extracts the coefficients of a given model using a FFT (np-fft).

Note that the coefficients are complex numbers, but the imaginary part of the coefficients should be very close to zero, since the expectation values of the Pauli operators are real numbers.

It can perform oversampling in both the frequency and time domain using the mfs and mts arguments.

Parameters:

Name	Type	Description	Default
model	Model	The model to sample.	required
mfs	int	Multiplicator for the highest frequency. Default is 2.	1
mts	int	Multiplicator for the number of time samples. Default is 1.	1
shift	bool	Whether to apply np-fftshift. Default is False.	False
trim	bool	Whether to remove the Nyquist frequency if spectrum is even. Default is False.	False
kwargs	Any	Additional keyword arguments for the model function.	{}

Returns:

Type	Description
ndarray	Tuple[np.ndarray, np.ndarray]: Tuple containing the coefficients
ndarray	and frequencies.

Source code in qml_essentials/coefficients.py

@staticmethod
def get_spectrum(
    model: Model,
    mfs: int = 1,
    mts: int = 1,
    shift=False,
    trim=False,
    **kwargs,
) -> Tuple[np.ndarray, np.ndarray]:
    """
    Extracts the coefficients of a given model using a FFT (np-fft).

    Note that the coefficients are complex numbers, but the imaginary part
    of the coefficients should be very close to zero, since the expectation
    values of the Pauli operators are real numbers.

    It can perform oversampling in both the frequency and time domain
    using the `mfs` and `mts` arguments.

    Args:
        model (Model): The model to sample.
        mfs (int): Multiplicator for the highest frequency. Default is 2.
        mts (int): Multiplicator for the number of time samples. Default is 1.
        shift (bool): Whether to apply np-fftshift. Default is False.
        trim (bool): Whether to remove the Nyquist frequency if spectrum is even.
            Default is False.
        kwargs (Any): Additional keyword arguments for the model function.

    Returns:
        Tuple[np.ndarray, np.ndarray]: Tuple containing the coefficients
        and frequencies.
    """
    kwargs.setdefault("force_mean", True)
    kwargs.setdefault("execution_type", "expval")

    coeffs, freqs = Coefficients._fourier_transform(
        model, mfs=mfs, mts=mts, **kwargs
    )

    if not np.isclose(np.sum(coeffs).imag, 0.0, rtol=1.0e-5):
        raise ValueError(
            f"Spectrum is not real. Imaginary part of coefficients is:\
            {np.sum(coeffs).imag}"
        )

    if trim:
        for ax in range(model.n_input_feat):
            if coeffs.shape[ax] % 2 == 0:
                coeffs = np.delete(coeffs, len(coeffs) // 2, axis=ax)
                freqs = np.delete(freqs, len(freqs) // 2, axis=ax)

    if shift:
        coeffs = np.fft.fftshift(coeffs, axes=list(range(model.n_input_feat)))
        freqs = np.fft.fftshift(freqs)

    return coeffs, freqs