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)

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.

Nota

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.

Parâmetros

• 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

Nota

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

Levanta

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)

Parâmetros

• 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

Nota

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

Levanta

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.

Tipo de retorno

Union[Backend, BaseBackend]

batch_data(data, labels=None, minibatch_size=- 1)

batch data

property class_to_label

returns class to label

cleanup_parameterized_circuits()

set parameterized circuits to None

construct_circuit(x, theta, measurement=False)

Construct circuit based on data and parameters in variational form.

Parâmetros

• 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

Retorna

the circuit

Tipo de retorno

QuantumCircuit

Levanta

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

property datapoints

return data points

property feature_map

Return the feature map.

Tipo de retorno

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.

Parâmetros

• 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

Retorna

Optimized variational parameters, and corresponding minimum cost value.

Tipo de retorno

dict

Levanta

ValueError – invalid input

get_optimal_circuit()

get optimal circuit

get_optimal_cost()

get optimal cost

get_optimal_vector()

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

Tipo de retorno

Optional[ndarray]

is_gradient_really_supported()

returns is gradient really supported

property label_to_class

returns label to class

load_model(file_path)

load model

property optimal_params

returns optimal parameters

property optimizer

Returns optimizer

Tipo de retorno

Optional[Optimizer]

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

Predict the labels for the data.

Parâmetros

• 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

Retorna

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)

Tipo de retorno

list

property quantum_instance

Returns quantum instance.

Tipo de retorno

Optional[QuantumInstance]

property random

Return a numpy random.

property ret

returns result

run(quantum_instance=None, **kwargs)

Execute the algorithm with selected backend.

Parâmetros

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

• kwargs (dict) – kwargs

Retorna

results of an algorithm.

Tipo de retorno

dict

Levanta

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

save_model(file_path)

save model

set_backend(backend, **kwargs)

Sets backend with configuration.

Tipo de retorno

None

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

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

Parâmetros

• 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

Retorna

classification accuracy

Tipo de retorno

float

property test_dataset

returns test dataset

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

Train the models, and save results.

Parâmetros

• 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

Tipo de retorno

Union[QuantumCircuit, VariationalForm, None]