squlearn.qnn.lowlevel_qnn_base.LowLevelQNNBase

class squlearn.qnn.lowlevel_qnn_base.LowLevelQNNBase(parameterized_quantum_circuit: EncodingCircuitBase, observable: ObservableBase | list, executor: Executor)

Base class for low-level QNNs.

Parameters:

• pqc (EncodingCircuitBase) – The parameterized quantum circuit.

• observable (Union[ObservableBase, list]) – The observable(s) to measure.

• executor (Executor) – The executor for the quantum circuit.

abstract evaluate(x: float | ndarray, param: float | ndarray, param_obs: float | ndarray, *values: str | ParameterVector | ParameterVectorElement | tuple) → dict

General function for evaluating the output of derivatives of the QNN.

Evaluation works for given combination of input features x and parameters param and param_op. The function includes caching of results

If x, param, and/or param_op are given as a nested list (for example multiple sets of parameters), the values are returned in a nested list.

Parameters:

• x (np.ndarray) – Values of the input feature data.

• param (np.ndarray) – Parameter values of the PQC parameters

• param_op (np.ndarray) – Parameter values of the operator parameters

• values – Derivatives (or values) of the QNN that are evaluated. Higher order derivatives are given as tuples of parameters or vectors.

Results:

Returns a dictionary with the computed values. The keys of the dictionary are given by the entries in the values tuple

abstract get_params(deep: bool = True) → dict

Get the parameters of the QNN.

gradient(x: float | ndarray, param: float | ndarray, param_obs: float | ndarray)

Return the gradient wrt. trainable parameters of the QNN.

Parameters:

• x (Union[float, np.ndarray]) – Input data.

• param (Union[float, np.ndarray]) – Parameters of the PQC.

• param_obs (Union[float, np.ndarray]) – Parameters of the observable.

Returns:

Gradient of the QNN.

Return type:

np.ndarray

abstract set_params(**params) → None

Set the parameters of the QNN.