Ansaetze

.. or Ansatzes as preferred by the english community. Anyway, we got various of the most-used Ansaetze implemented in this package.

You can load them manually by

from qml_essentials.ansaetze import Ansaetze
all_ansaetze = Ansaetze.get_available()

for ansatz in all_ansaetze:
    print(ansatz.__name__)

No_Ansatz
Circuit_1
Circuit_2
Circuit_3
Circuit_4
Circuit_6
Circuit_9
Circuit_10
Circuit_15
Circuit_16
Circuit_17
Circuit_18
Circuit_19
No_Entangling
Strongly_Entangling
Hardware_Efficient

Note that Circuit 10 deviates from the original implementation!

However, usually you just want reference to them (by name) when instantiating a model. To get an overview of all the available Ansaetze, checkout the references.

If you want to implement your own ansatz, you can do so by inheriting from the Circuit class:

from qml_essentials.ansaetze import Circuit

class MyHardwareEfficient(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        return n_qubits * 3

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

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, noise_params=None):
        w_idx = 0
        for q in range(n_qubits):
            qml.RY(w[w_idx], wires=q)
            w_idx += 1
            qml.RZ(w[w_idx], wires=q)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits - 1):
                qml.CZ(wires=[q, q + 1])

and then pass it to the model:

from qml_essentials.model import Model

model = Model(
    n_qubits=2,
    n_layers=1,
    circuit_type=MyHardwareEfficient,
)

Checkout page Usage on how to proceed from here.

You might have noticed, that the build method takes an additional input noise_params, which we did not used so far. In general, all of the Ansatzes, that are implemented in this package allow this additional input which is a dictionary containing all the noise parameters of the circuit (here all with probability \(0.0\)):

noise_params = {
    "BitFlip": 0.0,
    "PhaseFlip": 0.0,
    "AmplitudeDamping": 0.0,
    "PhaseDamping": 0.0,
    "DepolarizingChannel": 0.0,
}

Providing this optional input will apply the corresponding noise to the model where the Bit Flip, Phase Flip and Depolarizing Channel are applied after each gate and the Amplitude and Phase Damping are applied at the end of the circuit. To achieve this, we implement our own set of noisy gates, that build upon the Pennylane gates. To demonstrate this, let's extend our example above:

from qml_essentials.ansaetze import Gates, Circuit

class MyNoisyHardwareEfficient(Circuit):
    @staticmethod
    def n_params_per_layer(n_qubits: int) -> int:
        return n_qubits * 3

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

    @staticmethod
    def build(w: np.ndarray, n_qubits: int, noise_params=None):
        w_idx = 0
        for q in range(n_qubits):
            Gates.RY(w[w_idx], wires=q, noise_params=noise_params)
            w_idx += 1
            Gates.RZ(w[w_idx], wires=q, noise_params=noise_params)
            w_idx += 1

        if n_qubits > 1:
            for q in range(n_qubits - 1):
                Gates.CZ(wires=[q, q + 1], noise_params=noise_params)

As you can see, we slightly modified the example, by importing the Gates class from ansaetze and by adding the noise_params input to each of the gates. When using a noisy circuit, make sure to run the model with the density execution type:

_ = model(
    model.params,
    inputs=None,
    execution_type="density",
    noise_params={
        "BitFlip": 0.01,
        "PhaseFlip": 0.02,
        "AmplitudeDamping": 0.03,
        "PhaseDamping": 0.04,
        "DepolarizingChannel": 0.05,
    })