qleet.simulators package

Submodules

qleet.simulators.circuit_simulators module

Module to draw samples from the circuit. Used for computing properties of the circuit like Entanglability and Expressibility.

class qleet.simulators.circuit_simulators.CircuitSimulator(circuit: CircuitDescriptor, noise_model: Optional[Union[cirq.devices.noise_model.NoiseModel, qiskit.providers.aer.noise.NoiseModel, pyquil.noise.NoiseModel]] = None)

Bases: object

The interface for users to execute their CircuitDescriptor objects

property result: Optional[numpy.ndarray]

Get the results stored from the circuit simulator :return: stored result of the circuit simulation if it has been performed, else None. :rtype: np.array or None

simulate(param_resolver: Dict[qiskit.circuit.Parameter, float], shots: int = 1024) → numpy.ndarray

Simulate to get the state vector or the density matrix :type param_resolver: Dict to resolve all parameters to a static float value :param param_resolver: a dictionary of all the symbols/parameters mapping to their values :type shots: int :param shots: number of times to run the qiskit density matrix simulator :returns: state vector or density matrix resulting from the simulation :rtype: np.array :raises NotImplementedError: if circuit simulation is not supported for a backend

qleet.simulators.pqc_trainer module

The module which houses the Parametrized Quantum Circuit trainer class.

It generates the TensorFlow Quantum model, and allows Keras like API to train and evaluate a model.

class qleet.simulators.pqc_trainer.PQCSimulatedTrainer(circuit: CircuitDescriptor)

Bases: object

A class to train parametrized Quantum Circuits in Tensorflow Quantum Uses gradient descent over the provided parameters, using the TFQ Adjoin differentiator.

evaluate(n_samples: int = 1000) → float

Evaluates the Parametrized Quantum Circuit. :type n_samples: int :param n_samples: The number of samples to evaluate the circuit over :returns: The average loss of the circuit over all the samples :rtype: float

train(n_samples=100, loggers: Optional[AnalyzerList] = None) → tensorflow.keras.Model

Trains the parameter of the circuit to minimize the loss. :type n_samples: int :param n_samples: Number of samples to train the circuit over :type loggers: AnalyzerList :param loggers: The AnalyzerList that tracks the training of the model :returns: The trained model :rtype: tf.keras.Model

Module contents