pygsti.extras.drift.trmodel
Functions for Fourier analysis of equally spaced time-series data
Module Contents
Classes
Encapsulates a basic form of time-resolved model, for implementing simple types of time-resolved characterization, |
Functions
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The negative loglikelihood for a TimeResolvedModel given the time-series data. |
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Finds the maximum likelihood TimeResolvedModel given the data. |
- class pygsti.extras.drift.trmodel.TimeResolvedModel(hyperparameters, parameters)
Bases:
objectEncapsulates 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.
Returns
TimeResolvedModel
- set_parameters(parameters)
Sets the parameters of the model.
- parameters_copy()
Returns the parameters of the model.
- abstract 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
- circuitCircuit
The circuit to return the probability trajectories for.
- timeslist
The times to calculate the probabilities for.
Returns
- dict
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).
- copy()
- pygsti.extras.drift.trmodel.negloglikelihood(trmodel, ds, minp=0, maxp=1)
The negative loglikelihood for a TimeResolvedModel given the time-series data.
Parameters
- timeresolvedmodel: TimeResolvedModel
The TimeResolvedModel to calculate the likelihood of.
- ds: DataSet
A DataSet, containing time-series data.
- minp, maxp: float, optional
Value used to smooth the 0 and 1 probability boundaries for the likelihood function. To get the extact nll, leave as 0 and 1.
Returns
- float
The negative loglikelihood of the model.
- pygsti.extras.drift.trmodel.maxlikelihood(trmodel, ds, minp=0.0001, maxp=1 - 1e-06, bounds=None, returnoptout=False, optoptions=None, verbosity=1)
Finds the maximum likelihood TimeResolvedModel given the data.
Parameters
- timeresolvedmodel: TimeResolvedModel
The TimeResolvedModel that is used as the seed, and which defines the class of parameterized models to optimize over.
- ds: DataSet
A DataSet, containing time-series data.
- minp, maxp: float, optional
Value used to smooth the 0 and 1 probability boundaries for the likelihood function.
- bounds: list or None, optional
Bounds on the parameters, as specified in scipy.optimize.minimize
- optout: bool, optional
Wether to return the output of scipy.optimize.minimize
- optoptions: dict, optional
Optional arguments for scipy.optimize.minimize.
Returns
- float
The maximum loglikelihood model