pygsti.algorithms.rbfit
Functions for analyzing RB data
Module Contents
Classes
An object to contain the results from fitting RB data. 
Functions

Implements a "standard" leastsquares fit of RB data. 

Fits RB average success probabilities to the exponential decay a + Bp^m using leastsquares fitting. 
 pygsti.algorithms.rbfit.std_least_squares_fit(lengths, asps, n, seed=None, asymptote=None, ftype='full', rtype='EI')
Implements a “standard” leastsquares fit of RB data.
Fits the average success probabilities to the exponential decay A + Bp^m, using leastsquares fitting.
Parameters
 lengthslist
The RB lengths to fit to (the ‘m’ values in A + Bp^m).
 aspslist
The average survival probabilities to fit (the observed P_m values to fit to P_m = A + Bp^m).
 nint
The number of qubits the data was generated from.
 seedlist, optional
Seeds for the fit of B and p (A, if a variable, is seeded to the asymptote defined by asympote).
 asymptotefloat, optional
If not None, the A value for the fitting to A + Bp^m with A fixed. Defaults to 1/2^n. Note that this value is used even when fitting A; in that case B and p are estimated with A fixed to this value, and then this A and the estimated B and p are seed for the full fit.
 ftype{‘full’,’FA’,’full+FA’}, optional
The fit type to implement. ‘full’ corresponds to fitting all of A, B and p. ‘FA’ corresponds to fixing ‘A’ to the value specified by asymptote. ‘full+FA’ returns the results of both fits.
 rtype{‘EI’,’AGI’}, optional
The RB error rate rescaling convention. ‘EI’ results in RB error rates that are associated with the entanglement infidelity, which is the error probability with stochastic errors (and is equal to the diamond distance). ‘AGI’ results in RB error rates that are associated with average gate infidelity.
Returns
 Dict or Dicts
If ftype = ‘full’ or ftype = ‘FA’ then a dict containing the results of the relevant fit. If ftype = ‘full+FA’ then two dicts are returned. The first dict corresponds to the full fit and the second to the fixedasymptote fit.
 pygsti.algorithms.rbfit.custom_least_squares_fit(lengths, asps, n, a=None, b=None, seed=None, rtype='EI')
Fits RB average success probabilities to the exponential decay a + Bp^m using leastsquares fitting.
Parameters
 lengthslist
The RB lengths to fit to (the ‘m’ values in a + Bp^m).
 aspslist
The average survival probabilities to fit (the observed P_m values to fit to P_m = a + Bp^m).
 nint
The number of qubits the data was generated from.
 afloat, optional
If not None, a value to fix a to.
 bfloat, optional
If not None, a value to fix b to.
 seedlist, optional
Seeds for variables in the fit, in the order [a,b,p] (with a and/or b dropped if it is set to a fixed value).
 rtype{‘EI’,’AGI’}, optional
The RB error rate rescaling convention. ‘EI’ results in RB error rates that are associated with the entanglement infidelity, which is the error probability with stochastic errors (and is equal to the diamond distance). ‘AGI’ results in RB error rates that are associated with average gate infidelity.
Returns
 Dict
The fit results. If item with the key ‘success’ is False, the fit has failed.
 class pygsti.algorithms.rbfit.FitResults(fittype, seed, rtype, success, estimates, variable, stds=None, bootstraps=None, bootstraps_failrate=None)
Bases:
pygsti.baseobjs.nicelyserializable.NicelySerializable
An object to contain the results from fitting RB data.
Currently just a container for the results, and does not include any methods.
Parameters
 fittypestr
A string to identity the type of fit.
 seedlist
The seed used in the fitting.
 rtype{‘IE’,’AGI’}
The type of RB error rate that the ‘r’ in these fit results corresponds to.
 successbool
Whether the fit was successful.
 estimatesdict
A dictionary containing the estimates of all parameters
 variabledict
A dictionary that specifies which of the parameters in “estimates” where variables to estimate (set to True for estimated parameters, False for fixed constants). This is useful when fitting to A + B*p^m and fixing one or more of these parameters: because then the “estimates” dict can still be queried for all three parameters.
 stdsdict, optional
Estimated standard deviations for the parameters.
 bootstrapsdict, optional
Bootstrapped values for the estimated parameters, from which the standard deviations were calculated.
 bootstraps_failratefloat, optional
The proporition of the estimates of the parameters from bootstrapped dataset failed.
Initialize a FitResults object.
Parameters
 fittypestr
A string to identity the type of fit.
 seedlist
The seed used in the fitting.
 rtype{‘IE’,’AGI’}
The type of RB error rate that the ‘r’ in these fit results corresponds to.
 successbool
Whether the fit was successful.
 estimatesdict
A dictionary containing the estimates of all parameters
 variabledict
A dictionary that specifies which of the parameters in “estimates” where variables to estimate (set to True for estimated parameters, False for fixed constants). This is useful when fitting to A + B*p^m and fixing one or more of these parameters: because then the “estimates” dict can still be queried for all three parameters.
 stdsdict, optional
Estimated standard deviations for the parameters.
 bootstrapsdict, optional
Bootstrapped values for the estimated parameters, from which the standard deviations were calculated.
 bootstraps_failratefloat, optional
The proporition of the estimates of the parameters from bootstrapped dataset failed.