pygsti.extras.drift.trmodel.TimeResolvedModel

class TimeResolvedModel(hyperparameters, parameters)

Bases: object

Encapsulates a basic form of time-resolved model, for implementing simple types of time-resolved characterization, e.g., time-resolved Ramsey spectroscopy. This object is a container for specifying a particular time-resolved model, which is achieved by defining the method probabilities. See the docstring of that method for further details.

This object is not intended to be used to encapsulate a time-resolved model that requires any intensive computations, e.g., a time-resolved process matrix model for full time-resolved GST. Instead, it is intend to be used for easy DIY time-resolved tomography on very simple models.

Initializes a TimResolvedModel object.

Parameters:

• hyperparameters (list) – A set of meta-parameters, that define the model. For example, these could be frequencies to include in a Fourier decomposition.

• parameters (list) – The values for the parameters of the model. For example, these could be the amplitudes for each frequency in a Fourier decomposition.

Return type:

TimeResolvedModel

Methods

__init__(hyperparameters, parameters)	Initializes a TimResolvedModel object.
copy()
parameters_copy()	Returns the parameters of the model.
probabilities(circuit, times)	Specified in each derived class
set_parameters(parameters)	Sets the parameters of the model.

parameters_copy()

Returns the parameters of the model.

probabilities(circuit, times)

Specified in each derived class

Specifying this method is the core to building a time-resolved model. This method should return the probabiilties for each outcome, for the input circuit at the specified times.

Parameters:

• circuit (Circuit) – The circuit to return the probability trajectories for.

• times (list) – The times to calculate the probabilities for.

Returns:

A dictionary where the keys are the possible outcomes of the circuit, and the value for an outcome is a list of the probabilities to obtain that outcomes at the specified times (so this list is the same length as times).

Return type:

dict

set_parameters(parameters)

Sets the parameters of the model.