qiskit.aqua.components.neural_networks.QuantumGenerator

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

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.

매개변수

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

• num_qubits (List[int]) – 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

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

예외

AquaError – Set multivariate variational distribution to represent multivariate data

__init__(bounds, num_qubits, generator_circuit=None, init_params=None, optimizer=None, snapshot_dir=None)

매개변수

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

• num_qubits (List[int]) – 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

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

예외

AquaError – Set multivariate variational distribution to represent multivariate data

Methods

__init__(bounds, num_qubits[, …])	type bounds ndarray
construct_circuit([params])	Construct generator circuit.
get_output(quantum_instance[, params, shots])	Get classical data samples from the generator.
loss(x, weights)	Loss function for training the generator’s parameters.
set_discriminator(discriminator)	Set discriminator network.
set_seed(seed)	Set seed.
train([quantum_instance, shots])	Perform one training step w.r.t to the generator’s parameters

construct_circuit(params=None)

Construct generator circuit.

매개변수

params (list | dict) – parameters which should be used to run the generator.

반환값

construct the quantum circuit and return as gate

반환 형식

Instruction

get_output(quantum_instance, params=None, shots=None)

Get classical data samples from the generator. Running the quantum generator circuit results in a quantum state. To train this generator with a classical discriminator, we need to sample classical outputs by measuring the quantum state and mapping them to feature space defined by the training data.

매개변수

• quantum_instance (QuantumInstance) – Quantum Instance, used to run the generator circuit.

• params (numpy.ndarray) – array or None, parameters which should be used to run the generator, if None use self._params

• shots (int) – if not None use a number of shots that is different from the number set in quantum_instance

반환값

generated samples, array: sample occurrence in percentage

반환 형식

list

loss(x, weights)

Loss function for training the generator’s parameters.

매개변수

• x (numpy.ndarray) – sample label (equivalent to discriminator output)

• weights (numpy.ndarray) – probability for measuring the sample

반환값

loss function

반환 형식

float

set_discriminator(discriminator)

Set discriminator network.

매개변수

discriminator (Discriminator) – Discriminator used to compute the loss function.

set_seed(seed)

Set seed.

매개변수

seed (int) – seed

train(quantum_instance=None, shots=None)

Perform one training step w.r.t to the generator’s parameters

매개변수

• quantum_instance (QuantumInstance) – used to run the generator circuit.

• shots (int) – Number of shots for hardware or qasm execution.

반환값

generator loss(float) and updated parameters (array).

반환 형식

dict