squlearn.qnn.base_qnn.BaseQNN

class squlearn.qnn.base_qnn.BaseQNN(encoding_circuit: EncodingCircuitBase, operator: ObservableBase | list[ObservableBase], executor: Executor, loss: LossBase, optimizer: OptimizerBase, param_ini: ndarray | None = None, param_op_ini: ndarray | None = None, batch_size: int = None, epochs: int = None, shuffle: bool = None, opt_param_op: bool = True, variance: float | Callable = None, shot_control: ShotControlBase = None, parameter_seed: int | None = 0, caching: bool = True, pretrained: bool = False, callback: Callable | str | None = None, primitive: str | None = None, **kwargs)

Base Class for Quantum Neural Networks.

Parameters:

  • encoding_circuit – Parameterized quantum circuit in encoding circuit format

  • operator – Operator that are used in the expectation value of the QNN. Can be a list for multiple outputs.

  • executor – Executor instance

  • optimizer – Optimizer instance

  • param_ini – Initialization values of the parameters of the PQC

  • param_op_ini – Initialization values of the cost operator

  • batch_size – Number of data points in each batch, for SGDMixin optimizers

  • epochs – Number of epochs of SGD to perform, for SGDMixin optimizers

  • shuffle – If True, data points get shuffled before each epoch (default: False), for SGDMixin optimizers

  • opt_param_op – If True, operators parameters get optimized

  • variance – Variance factor

  • parameter_seed – Seed for the random number generator for the parameter initialization

  • caching – If True, the results of the QNN are cached.

  • pretrained – Set to true if the supplied parameters are already trained.

  • callback (Union[Callable, str, None], default=None) – A callback for the optimization loop. Can be either a Callable, “pbar” (which uses a tqdm.tqdm process bar) or None. If None, the optimizers (default) callback will be used.

  • primitive – The Qiskit primitive that is utilized in the qnn, if a Qiskit backend is used in the executor (not supported for PennyLane backends) Default primitive is the one specified in the executor initialization, if nothing is specified, the estimator will used. Possible values are "estimator" or "sampler".

fit(X, y, weights: ndarray = None) → None

Fit a new model to data.

This method will reinitialize the models parameters and fit it to the provided data.

Parameters:

  • X – array-like or sparse matrix of shape (n_samples, n_features) Input data

  • y – array-like of shape (n_samples,) Labels

  • weights – Weights for each data point

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep: bool = True) → dict

Returns a dictionary of parameters for the current object.

Parameters:

deep – If True, includes the parameters from the base class.

Returns:

A dictionary of parameters for the current object.

Return type:

dict

set_fit_request(*, weights: bool | None | str = '$UNCHANGED$') → BaseQNN

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for weights parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params) → BaseQNN

Sets the hyper-parameters of the BaseQNN.

Parameters:

params – Hyper-parameters of the BaseQNN.

Returns:

updated BaseQNN