VQC

class VQC(optimizer, feature_map, var_form, training_dataset, test_dataset=None, datapoints=None, max_evals_grouped=1, minibatch_size=- 1, callback=None, quantum_instance=None)

The Variational Quantum Classifier algorithm.

Similar to QSVM, the VQC algorithm also applies to classification problems. VQC uses the variational method to solve such problems in a quantum processor. Specifically, it optimizes a parameterized quantum circuit to provide a solution that cleanly separates the data.

Note

The VQC stores the parameters of var_form and feature_map sorted by name to map the values provided by the optimizer to the circuit. This is done to ensure reproducible results, for example such that running the optimization twice with same random seeds yields the same result.

Parameters

• optimizer (Optimizer) – The classical optimizer to use.

• feature_map (Union[QuantumCircuit, FeatureMap]) – The FeatureMap instance to use.

• var_form (Union[QuantumCircuit, VariationalForm]) – The variational form instance.

• training_dataset (Dict[str, ndarray]) – The training dataset, in the format {‘A’: np.ndarray, ‘B’: np.ndarray, …}.

• test_dataset (Optional[Dict[str, ndarray]]) – The test dataset, in same format as training_dataset.

• datapoints (Optional[ndarray]) – NxD array, N is the number of data and D is data dimension.

• max_evals_grouped (int) – The maximum number of evaluations to perform simultaneously.

• minibatch_size (int) – The size of a mini-batch.

• callback (Optional[Callable[[int, ndarray, float, int], None]]) – a callback that can access the intermediate data during the optimization. Four parameter values are passed to the callback as follows during each evaluation. These are: the evaluation count, parameters of the variational form, the evaluated value, the index of data batch.

• quantum_instance (Union[QuantumInstance, BaseBackend, None]) – Quantum Instance or Backend

Note

We use label to denotes numeric results and class the class names (str).

Raises

AquaError – Missing feature map or missing training dataset.

Attributes

VQC.backend	Returns backend.
VQC.class_to_label	returns class to label
VQC.datapoints	return data points
VQC.feature_map	Return the feature map.
VQC.initial_point	Returns initial point
VQC.label_to_class	returns label to class
VQC.optimal_params	returns optimal parameters
VQC.optimizer	Returns optimizer
VQC.quantum_instance	Returns quantum instance.
VQC.random	Return a numpy random.
VQC.ret	returns result
VQC.test_dataset	returns test dataset
VQC.training_dataset	returns training dataset
VQC.var_form	Returns variational form

Methods

VQC.batch_data(data[, labels, minibatch_size])	batch data
VQC.cleanup_parameterized_circuits()	set parameterized circuits to None
VQC.construct_circuit(x, theta[, measurement])	Construct circuit based on data and parameters in variational form.
VQC.find_minimum([initial_point, var_form, …])	Optimize to find the minimum cost value.
VQC.get_optimal_circuit()	get optimal circuit
VQC.get_optimal_cost()	get optimal cost
VQC.get_optimal_vector()	get optimal vector
VQC.get_prob_vector_for_params(…[, …])	Helper function to get probability vectors for a set of params
VQC.get_probabilities_for_counts(counts)	get probabilities for counts
VQC.is_gradient_really_supported()	returns is gradient really supported
VQC.load_model(file_path)	load model
VQC.predict(data[, quantum_instance, …])	Predict the labels for the data.
VQC.run([quantum_instance])	Execute the algorithm with selected backend.
VQC.save_model(file_path)	save model
VQC.set_backend(backend, **kwargs)	Sets backend with configuration.
VQC.test(data, labels[, quantum_instance, …])	Predict the labels for the data, and test against with ground truth labels.
VQC.train(data, labels[, quantum_instance, …])	Train the models, and save results.