qiskit.aqua.algorithms.QGAN¶

class QGAN(data, bounds=None, num_qubits=None, batch_size=500, num_epochs=3000, seed=7, discriminator=None, generator=None, tol_rel_ent=None, snapshot_dir=None, quantum_instance=None)[código fonte]¶

The Quantum Generative Adversarial Network algorithm.

The qGAN [1] is a hybrid quantum-classical algorithm used for generative modeling tasks.

This adaptive algorithm uses the interplay of a generative GenerativeNetwork and a discriminative DiscriminativeNetwork network to learn the probability distribution underlying given training data.

These networks are trained in alternating optimization steps, where the discriminator tries to differentiate between training data samples and data samples from the generator and the generator aims at generating samples which the discriminator classifies as training data samples. Eventually, the quantum generator learns the training data’s underlying probability distribution. The trained quantum generator loads a quantum state which is a model of the target distribution.

References:

[1] Zoufal et al.,: Quantum Generative Adversarial Networks for learning and loading random distributions

Parâmetros

data (ndarray) – Training data of dimension k
bounds (Optional[ndarray]) – k min/max data values [[min_0,max_0],…,[min_k-1,max_k-1]] if univariate data: [min_0,max_0]
num_qubits (Optional[ndarray]) – k numbers of qubits to determine representation resolution, i.e. n qubits enable the representation of 2**n values [num_qubits_0,…, num_qubits_k-1]
batch_size (int) – Batch size, has a min. value of 1.
num_epochs (int) – Number of training epochs
seed (int) – Random number seed
discriminator (Optional[DiscriminativeNetwork]) – Discriminates between real and fake data samples
generator (Optional[GenerativeNetwork]) – Generates ‘fake’ data samples
tol_rel_ent (Optional[float]) – Set tolerance level for relative entropy. If the training achieves relative entropy equal or lower than tolerance it finishes.
snapshot_dir (Optional[str]) – Directory in to which to store cvs file with parameters, if None (default) then no cvs file is created.
quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – Quantum Instance or Backend

Levanta

AquaError – invalid input

__init__(data, bounds=None, num_qubits=None, batch_size=500, num_epochs=3000, seed=7, discriminator=None, generator=None, tol_rel_ent=None, snapshot_dir=None, quantum_instance=None)[código fonte]¶

Parâmetros

data (ndarray) – Training data of dimension k
bounds (Optional[ndarray]) – k min/max data values [[min_0,max_0],…,[min_k-1,max_k-1]] if univariate data: [min_0,max_0]
num_qubits (Optional[ndarray]) – k numbers of qubits to determine representation resolution, i.e. n qubits enable the representation of 2**n values [num_qubits_0,…, num_qubits_k-1]
batch_size (int) – Batch size, has a min. value of 1.
num_epochs (int) – Number of training epochs
seed (int) – Random number seed
discriminator (Optional[DiscriminativeNetwork]) – Discriminates between real and fake data samples
generator (Optional[GenerativeNetwork]) – Generates ‘fake’ data samples
tol_rel_ent (Optional[float]) – Set tolerance level for relative entropy. If the training achieves relative entropy equal or lower than tolerance it finishes.
snapshot_dir (Optional[str]) – Directory in to which to store cvs file with parameters, if None (default) then no cvs file is created.
quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – Quantum Instance or Backend

Levanta

AquaError – invalid input

Methods

`__init__`(data[, bounds, num_qubits, …])	type data `ndarray`
`get_rel_entr`()	Get relative entropy between target and trained distribution
`run`([quantum_instance])	Execute the algorithm with selected backend.
`set_backend`(backend, **kwargs)	Sets backend with configuration.
`set_discriminator`([discriminator])	Initialize discriminator.
`set_generator`([generator_circuit, …])	Initialize generator.
`train`()	Train the qGAN

Attributes

`backend`	Returns backend.
`d_loss`	Returns discriminator loss
`discriminator`	Returns discriminator
`g_loss`	Returns generator loss
`generator`	Returns generator
`quantum_instance`	Returns quantum instance.
`random`	Return a numpy random.
`rel_entr`	Returns relative entropy between target and trained distribution
`seed`	Returns random seed
`tol_rel_ent`	Returns tolerance for relative entropy

property backend¶

Returns backend.

Tipo de retorno: Union[Backend, BaseBackend]

property d_loss¶

Returns discriminator loss

Tipo de retorno: List[float]

property discriminator¶: Returns discriminator

property g_loss¶

Returns generator loss

Tipo de retorno: List[float]

property generator¶: Returns generator

get_rel_entr()[código fonte]¶

Get relative entropy between target and trained distribution

Tipo de retorno: float

property quantum_instance¶

Returns quantum instance.

Tipo de retorno: Optional[QuantumInstance]

property random¶: Return a numpy random.

property rel_entr¶

Returns relative entropy between target and trained distribution

Tipo de retorno: List[float]

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

property seed¶: Returns random seed

set_backend(backend, **kwargs)¶

Sets backend with configuration.

Tipo de retorno: None

set_discriminator(discriminator=None)[código fonte]¶

Initialize discriminator.

Parâmetros: discriminator (Discriminator) – discriminator

set_generator(generator_circuit=None, generator_init_params=None, generator_optimizer=None)[código fonte]¶

Initialize generator.

Parâmetros

generator_circuit (Union[UnivariateVariationalDistribution, MultivariateVariationalDistribution, QuantumCircuit, None]) – parameterized quantum circuit which sets the structure of the quantum generator
generator_init_params (Optional[ndarray]) – initial parameters for the generator circuit
generator_optimizer (Optional[Optimizer]) – optimizer to be used for the training of the generator

property tol_rel_ent¶: Returns tolerance for relative entropy

train()[código fonte]¶

Train the qGAN

Levanta: AquaError – Batch size bigger than the number of items in the truncated data set