VQC

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

Bases: qiskit.aqua.algorithms.vq_algorithm.VQAlgorithm

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.

• use_sigmoid_cross_entropy (bool) – whether to use sigmoid cross entropy or not.

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

Note

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

Raises

AquaError – Missing feature map or missing training dataset.

Methods

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

Attributes

backend

Returns backend.

Return type

Union[Backend, BaseBackend]

class_to_label

returns class to label

datapoints

return data points

feature_map

Return the feature map.

Return type

Union[FeatureMap, QuantumCircuit, None]

initial_point

Returns initial point

Return type

Optional[ndarray]

label_to_class

returns label to class

optimal_params

returns optimal parameters

optimizer

Returns optimizer

Return type

Optional[Optimizer]

quantum_instance

Returns quantum instance.

Return type

Optional[QuantumInstance]

random

Return a numpy random.

ret

returns result

test_dataset

returns test dataset

training_dataset

returns training dataset

var_form

Returns variational form

Return type

Union[QuantumCircuit, VariationalForm, None]