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)
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,Backend,BaseBackend,None]) – Quantum Instance or Backend
Raises
AquaError – invalid input
__init__
__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)
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,Backend,BaseBackend,None]) – Quantum Instance or Backend
Raises
AquaError – invalid input
Methods
__init__(data[, bounds, num_qubits, …]) | type datandarray |
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 |
backend
Returns backend.
Return type
Union[Backend, BaseBackend]
d_loss
Returns discriminator loss
Return type
List[float]
discriminator
Returns discriminator
g_loss
Returns generator loss
Return type
List[float]
generator
Returns generator
get_rel_entr
get_rel_entr()
Get relative entropy between target and trained distribution
Return type
float
quantum_instance
Returns quantum instance.
Return type
Optional[QuantumInstance]
random
Return a numpy random.
rel_entr
Returns relative entropy between target and trained distribution
Return type
List[float]
run
run(quantum_instance=None, **kwargs)
Execute the algorithm with selected backend.
Parameters
- quantum_instance (
Union[QuantumInstance,Backend,BaseBackend,None]) – the experimental setting. - kwargs (dict) – kwargs
Returns
results of an algorithm.
Return type
dict
Raises
AquaError – If a quantum instance or backend has not been provided
seed
Returns random seed
set_backend
set_backend(backend, **kwargs)
Sets backend with configuration.
Return type
None
set_discriminator
set_discriminator(discriminator=None)
Initialize discriminator.
Parameters
discriminator (Discriminator) – discriminator
set_generator
set_generator(generator_circuit=None, generator_init_params=None, generator_optimizer=None)
Initialize generator.
Parameters
- 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
tol_rel_ent
Returns tolerance for relative entropy
train
train()
Train the qGAN
Raises
AquaError – Batch size bigger than the number of items in the truncated data set