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qiskit.aqua.algorithms.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)[source]

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, Backend, 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.

__init__(optimizer, feature_map, var_form, training_dataset, test_dataset=None, datapoints=None, max_evals_grouped=1, minibatch_size=- 1, callback=None, quantum_instance=None)[source]
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, Backend, 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.

Methods

__init__(optimizer, feature_map, var_form, …)

type optimizer

Optimizer

batch_data(data[, labels, minibatch_size])

batch data

cleanup_parameterized_circuits()

set parameterized circuits to None

construct_circuit(x, theta[, measurement])

Construct circuit based on data and parameters in variational form.

find_minimum([initial_point, var_form, …])

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(counts)

get probabilities for counts

is_gradient_really_supported()

returns is gradient really supported

load_model(file_path)

load model

predict(data[, quantum_instance, …])

Predict the labels for the data.

run([quantum_instance])

Execute the algorithm with selected backend.

save_model(file_path)

save model

set_backend(backend, **kwargs)

Sets backend with configuration.

test(data, labels[, quantum_instance, …])

Predict the labels for the data, and test against with ground truth labels.

train(data, labels[, quantum_instance, …])

Train the models, and save results.

Attributes

backend

Returns backend.

class_to_label

returns class to label

datapoints

return data points

feature_map

Return the feature map.

initial_point

Returns initial point

label_to_class

returns label to class

optimal_params

returns optimal parameters

optimizer

Returns optimizer

quantum_instance

Returns quantum instance.

random

Return a numpy random.

ret

returns result

test_dataset

returns test dataset

training_dataset

returns training dataset

var_form

Returns variational form

property backend

Returns backend.

Return type

Union[Backend, BaseBackend]

batch_data(data, labels=None, minibatch_size=- 1)[source]

batch data

property class_to_label

returns class to label

cleanup_parameterized_circuits()

set parameterized circuits to None

construct_circuit(x, theta, measurement=False)[source]

Construct circuit based on data and parameters in variational form.

Parameters
  • x (numpy.ndarray) – 1-D array with D dimension

  • theta (list[numpy.ndarray]) – list of 1-D array, parameters sets for variational form

  • measurement (bool) – flag to add measurement

Returns

the circuit

Return type

QuantumCircuit

Raises

AquaError – If x and theta share parameters with the same name.

property datapoints

return data points

property feature_map

Return the feature map.

Return type

Union[FeatureMap, QuantumCircuit, None]

find_minimum(initial_point=None, var_form=None, cost_fn=None, optimizer=None, gradient_fn=None)

Optimize to find the minimum cost value.

Parameters
  • initial_point (Optional[ndarray]) – If not None will be used instead of any initial point supplied via constructor. If None and None was supplied to constructor then a random point will be used if the optimizer requires an initial point.

  • var_form (Union[QuantumCircuit, VariationalForm, None]) – If not None will be used instead of any variational form supplied via constructor.

  • cost_fn (Optional[Callable]) – If not None will be used instead of any cost_fn supplied via constructor.

  • optimizer (Optional[Optimizer]) – If not None will be used instead of any optimizer supplied via constructor.

  • gradient_fn (Optional[Callable]) – Optional gradient function for optimizer

Returns

Optimized variational parameters, and corresponding minimum cost value.

Return type

dict

Raises

ValueError – invalid input

get_optimal_circuit()[source]

get optimal circuit

get_optimal_cost()[source]

get optimal cost

get_optimal_vector()[source]

get optimal vector

get_prob_vector_for_params(construct_circuit_fn, params_s, quantum_instance, construct_circuit_args=None)

Helper function to get probability vectors for a set of params

get_probabilities_for_counts(counts)

get probabilities for counts

property initial_point

Returns initial point

Return type

Optional[ndarray]

is_gradient_really_supported()[source]

returns is gradient really supported

property label_to_class

returns label to class

load_model(file_path)[source]

load model

property optimal_params

returns optimal parameters

property optimizer

Returns optimizer

Return type

Optional[Optimizer]

predict(data, quantum_instance=None, minibatch_size=- 1, params=None)[source]

Predict the labels for the data.

Parameters
  • data (numpy.ndarray) – NxD array, N is number of data, D is data dimension

  • quantum_instance (QuantumInstance) – quantum backend with all setting

  • minibatch_size (int) – the size of each minibatched accuracy evaluation

  • params (list) – list of parameters to populate in the variational form

Returns

for each data point, generates the predicted probability for each class list: for each data point, generates the predicted label (that with the highest prob)

Return type

list

property quantum_instance

Returns quantum instance.

Return type

Optional[QuantumInstance]

property random

Return a numpy random.

property ret

returns result

run(quantum_instance=None, **kwargs)

Execute the algorithm with selected backend.

Parameters
  • quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – the experimental setting.

  • kwargs (dict) – kwargs

Returns

results of an algorithm.

Return type

dict

Raises

AquaError – If a quantum instance or backend has not been provided

save_model(file_path)[source]

save model

set_backend(backend, **kwargs)

Sets backend with configuration.

Return type

None

test(data, labels, quantum_instance=None, minibatch_size=- 1, params=None)[source]

Predict the labels for the data, and test against with ground truth labels.

Parameters
  • data (numpy.ndarray) – NxD array, N is number of data and D is data dimension

  • labels (numpy.ndarray) – Nx1 array, N is number of data

  • quantum_instance (QuantumInstance) – quantum backend with all setting

  • minibatch_size (int) – the size of each minibatched accuracy evaluation

  • params (list) – list of parameters to populate in the variational form

Returns

classification accuracy

Return type

float

property test_dataset

returns test dataset

train(data, labels, quantum_instance=None, minibatch_size=- 1)[source]

Train the models, and save results.

Parameters
  • data (numpy.ndarray) – NxD array, N is number of data and D is dimension

  • labels (numpy.ndarray) – Nx1 array, N is number of data

  • quantum_instance (QuantumInstance) – quantum backend with all setting

  • minibatch_size (int) – the size of each minibatched accuracy evaluation

property training_dataset

returns training dataset

property var_form

Returns variational form

Return type

Union[QuantumCircuit, VariationalForm, None]