QuantumGenerator
class QuantumGenerator(bounds, num_qubits, generator_circuit=None, init_params=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.
Parameters
- 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. - snapshot_dir (
Optional[str]) – str or None, if not None save the optimizer’s parameter after every update step to the given directory
Raises
AquaError – Set multivariate variational distribution to represent multivariate data
Methods
construct_circuit
QuantumGenerator.construct_circuit(params=None)
Construct generator circuit.
Parameters
params (numpy.ndarray) – parameters which should be used to run the generator, if None use self._params
Returns
construct the quantum circuit and return as gate
Return type
get_output
QuantumGenerator.get_output(quantum_instance, qc_state_in=None, 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.
Parameters
- quantum_instance (QuantumInstance) – Quantum Instance, used to run the generator circuit.
- qc_state_in (QuantumCircuit) – deprecated
- 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
Returns
generated samples, array: sample occurrence in percentage
Return type
list
loss
QuantumGenerator.loss(x, weights)
Loss function for training the generator’s parameters.
Parameters
- x (numpy.ndarray) – sample label (equivalent to discriminator output)
- weights (numpy.ndarray) – probability for measuring the sample
Returns
loss function
Return type
float
set_discriminator
QuantumGenerator.set_discriminator(discriminator)
Set discriminator network.
Parameters
discriminator (Discriminator) – Discriminator used to compute the loss function.
set_seed
QuantumGenerator.set_seed(seed)
Set seed.
Parameters
seed (int) – seed
train
QuantumGenerator.train(quantum_instance=None, shots=None)
Perform one training step w.r.t to the generator’s parameters
Parameters
- quantum_instance (QuantumInstance) – used to run the generator circuit.
- shots (int) – Number of shots for hardware or qasm execution.
Returns
generator loss(float) and updated parameters (array).
Return type
dict