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)[source]¶

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

Parameters

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, BaseBackend, None]) – Quantum Instance or Backend

Raises

AquaError – invalid input

Attributes

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

Methods

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