QuantumGenerator

class QuantumGenerator(bounds, num_qubits, generator_circuit=None, init_params=None, optimizer=None, gradient_function=None, snapshot_dir=None)

Bases: qiskit.aqua.components.neural_networks.generative_network.GenerativeNetwork

Quantum Generator.

The quantum generator is a parametrized quantum circuit which can be trained with the QGAN algorithm to generate a quantum state which approximates the probability distribution of given training data. At the beginning of the training the parameters will be set randomly, thus, the output will is random. Throughout the training the quantum generator learns to represent the target distribution. Eventually, the trained generator can be used for state preparation e.g. in QAE.

Parameters

• bounds (ndarray) – k min/max data values [[min_1,max_1],…,[min_k,max_k]], given input data dim k

• num_qubits (Union[List[int], ndarray]) – k numbers of qubits to determine representation resolution, i.e. n qubits enable the representation of 2**n values [n_1,…, n_k]

• generator_circuit (Union[UnivariateVariationalDistribution, MultivariateVariationalDistribution, QuantumCircuit, None]) – a UnivariateVariationalDistribution for univariate data, a MultivariateVariationalDistribution for multivariate data, or a QuantumCircuit implementing the generator.

• init_params (Union[List[float], ndarray, None]) – 1D numpy array or list, Initialization for the generator’s parameters.

• optimizer (Optional[Optimizer]) – optimizer to be used for the training of the generator

• gradient_function (Union[Callable, Gradient, None]) – A Gradient object, or a function returning partial derivatives of the loss function w.r.t. the generator variational params.

• snapshot_dir (Optional[str]) – str or None, if not None save the optimizer’s parameter after every update step to the given directory

Raises

AquaError – Set multivariate variational distribution to represent multivariate data

Methods

construct_circuit	Construct generator circuit.
get_output	Get classical data samples from the generator.
loss	Loss function for training the generator’s parameters.
set_discriminator	Set discriminator network.
set_seed	Set seed.
train	Perform one training step w.r.t to the generator’s parameters

Attributes

discriminator

Get discriminator.

Return type

DiscriminativeNetwork

optimizer

Get optimizer.

Return type

Optimizer

parameter_values

Get parameter values from the quantum generator

Return type

Union[List, ndarray]

Returns

Current parameter values

seed

Get seed.

Return type

int