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, use_sigmoid_cross_entropy=False, quantum_instance=None)[código fonte]¶

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.
use_sigmoid_cross_entropy (bool) – whether to use sigmoid cross entropy or not.
quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – Quantum Instance or Backend

Nota

We use label to denote 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, use_sigmoid_cross_entropy=False, quantum_instance=None)[código fonte]¶

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.
use_sigmoid_cross_entropy (bool) – whether to use sigmoid cross entropy or not.
quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – Quantum Instance or Backend

Nota

We use label to denote 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)[código fonte]¶: batch data

property class_to_label¶: returns class to label

cleanup_parameterized_circuits()¶: set parameterized circuits to None

construct_circuit(x, theta, measurement=False)[código fonte]¶

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()[código fonte]¶: get optimal circuit

get_optimal_cost()[código fonte]¶: get optimal cost

get_optimal_vector()[código fonte]¶: 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()[código fonte]¶: returns is gradient really supported

property label_to_class¶: returns label to class

load_model(file_path)[código fonte]¶: 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)[código fonte]¶

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)[código fonte]¶: 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)[código fonte]¶

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)[código fonte]¶

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]