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Usage#

Central component of our package is the Fourier model which you can import with 

from qml_essentials.model import Model

In the simplest scenario, one would instantiate such a model with $2$ qubits and a single layer using the "Hardware Efficient" ansatz by:

model = Model(
    n_qubits=2
    n_layers=1
    circuit_type="HardwareEfficient"
)

You can take a look at your model, by simply calling

print(model)

You can also provide a custom circuit, by instantiating from the Circuit class in qml_essentials.ansaetze.Circuit. See page "Ansaetze" for more details.

Calling the model without any (None) values for the params and inputs argument, will implicitly call the model with the recently (or initial) parameters and 0s as input.

In the following we will describe some concepts of this class. For a more detailled reference on the methods and arguments that are available, please see the references page.

The essentials#

There is much more to this package, than just providing a Fourier model. You can calculate the Expressibility or Entangling Capability besides the Coefficients which are unique to this kind of QML interpretation. Also checkout the available Ansaetze that we provide with this package.

Output Shape#

The output shape is determined by the output_qubit argument, provided in the instantiation of the model. When set to -1 all qubits are measured which will result in the shape being of size $n$ by default (depending on the execution type, see below).

If force_mean flag is set when calling the model, the output is averaged to a single value (while keeping the batch dimension).

Execution Type#

Our model be simulated in different ways by setting the execution_type property, when calling the model, to: - exp_val: Returns the expectation value between $0$ and $1$ - density: Calculates the density matrix - probs: Simulates the model with the number of shots, set by model.shots

Noise#

Noise can be added to the model by providing a noise_params argument, when calling the model, which is a dictionary with following keys - BitFlip - PhaseFlip - AmplitudeDamping - PhaseDamping - DepolarizingChannel with values between $0$ and $1$.

This will apply the corresponding noise in each layer with the provided factor.

Caching#

To speed up calculation, you can add cache=True when calling the model. The result of the model call will then be stored in a numpy format in a folder .cache. Each result is being identified by a md5 hash that is a representation of the following model properties: - number of qubits - number of layers - ansatz - data-reuploading flag - parameters - noise parameters - execution type - inputs - output qubit(s)