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, penalty=False)

기반 클래스: object

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

매개변수:

• data (ndarray | List) – Training data of dimension k

• bounds (ndarray | List | None) – k min/max data values [[min_0,max_0],…,[min_k-1,max_k-1]] if univariate data: [min_0,max_0]

• num_qubits (ndarray | List | None) – 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 (DiscriminativeNetwork | None) – Discriminates between real and fake data samples

• generator (GenerativeNetwork | None) – Generates ‘fake’ data samples

• tol_rel_ent (float | None) – Set tolerance level for relative entropy. If the training achieves relative entropy equal or lower than tolerance it finishes.

• snapshot_dir (str | None) – Directory in to which to store cvs file with parameters, if None (default) then no cvs file is created.

• quantum_instance (Backend | QuantumInstance | None) – Quantum Instance or Backend

• penalty (bool | None) – enable or not the gradient penalty in the discriminator

예외 발생:

QiskitMachineLearningError – invalid input

Attributes

backend	Returns backend.
d_loss	Returns discriminator loss
discriminator	Returns discriminator
g_loss	Returns generator loss
generator	Returns generator
penalty	Returns penalty parameter for the discriminator training
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

Methods

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