qiskit.ignis.verification.PurityRBFitter

class PurityRBFitter(purity_result, npurity, cliff_lengths, rb_pattern=None)

Class for fitter for purity RB.

Derived from RBFitterBase class.

Parameters

• purity_result (list) – list of results of the 3^n purity RB sequences per seed (qiskit.Result).

• npurity (int) – equals 3^n (where n is the dimension).

• cliff_lengths (list) – the Clifford lengths, 2D list i x j where i is the number of patterns, j is the number of cliffords lengths.

• rb_pattern (list) – the pattern for the RB sequences.

__init__(purity_result, npurity, cliff_lengths, rb_pattern=None)

Parameters

• purity_result (list) – list of results of the 3^n purity RB sequences per seed (qiskit.Result).

• npurity (int) – equals 3^n (where n is the dimension).

• cliff_lengths (list) – the Clifford lengths, 2D list i x j where i is the number of patterns, j is the number of cliffords lengths.

• rb_pattern (list) – the pattern for the RB sequences.

Methods

F234(n, a, b)	Function than maps: 2^n x 3^n –> 4^n , namely: (a,b) –> c where a in 2^n, b in 3^n, c in 4^n
__init__(purity_result, npurity, cliff_lengths)	param purity_result list of results of the
add_data(new_purity_result[, rerun_fit])	Add a new result.
add_zdict_ops()	Creating all Z-correlators in order to compute the expectation values.
calc_data()	Retrieve probabilities of success from execution results.
calc_statistics()	Extract averages and std dev from the raw data (self._raw_data).
fit_data()	Fit the Purity RB results to an exponential curve.
fit_data_pattern(patt_ind, fit_guess)	Fit the RB results of a particular pattern to an exponential curve.
plot_rb_data([pattern_index, ax, add_label, …])	Plot purity RB data of a single pattern.

Attributes

cliff_lengths	Return clifford lengths.
fit	Return the purity fit parameters.
raw_data	Return raw data.
rb_fit_fun	Return the fit function rb_fit_fun.
rbfit_pur	Return the purity RB fitter.
results	Return all the results.
seeds	Return the number of loaded seeds.
ydata	Return ydata (means and std devs).

static F234(n, a, b)

Function than maps: 2^n x 3^n –> 4^n , namely: (a,b) –> c where a in 2^n, b in 3^n, c in 4^n

add_data(new_purity_result, rerun_fit=True)

Add a new result.

Parameters

• new_purity_result (list) – list of RB results of the purity RB circuits.

• rerun_fit (bool) – re-calculate the means and fit the result.

Additional information:

Assumes that the executed ‘result’ is the output of circuits generated by randomized_benchmarking_seq where is_purity = True.

add_zdict_ops()

Creating all Z-correlators in order to compute the expectation values.

calc_data()

Retrieve probabilities of success from execution results.

Measure the purity calculation into an internal variable _raw_data which is a 3-dimensional list, where item (i,j,k) is the purity of the set of qubits in pattern “i” for seed no. j and vector length self._cliff_lengths[i][k].

Additional information:

Assumes that the executed ‘result’ is the output of circuits generated by randomized_benchmarking_seq,

calc_statistics()

Extract averages and std dev from the raw data (self._raw_data).

Assumes that self._calc_data has been run. Output into internal _ydata variable. ydata is a list of dictionaries (length number of patterns):

Dictionary ydata[i]:

• ydata[i][‘mean’] is a numpy_array of length n; entry j of this array contains the mean probability of success over seeds, for vector length self._cliff_lengths[i][j].

• ydata[i][‘std’] is a numpy_array of length n; entry j of this array contains the std of the probability of success over seeds, for vector length self._cliff_lengths[i][j].

property cliff_lengths

Return clifford lengths.

property fit

Return the purity fit parameters.

fit_data()

Fit the Purity RB results to an exponential curve.

Use the data to construct guess values for the fits.

Puts the results into a list of fit dictionaries where each dictionary corresponds to a pattern and has fields:

• params - three parameters of rb_fit_fun. The middle one is the exponent.

• err - the error limits of the parameters.

• epc - Error per Clifford.

• pepc - Purity Error per Clifford.

fit_data_pattern(patt_ind, fit_guess)

Fit the RB results of a particular pattern to an exponential curve.

Parameters

• patt_ind (int) – index of the subsystem to fit.

• fit_guess (list) – guess values for the fit.

Puts the results into a list of fit dictionaries where each dictionary corresponds to a pattern and has fields:

• params - three parameters of rb_fit_fun. The middle one is the exponent.

• err - the error limits of the parameters.

plot_rb_data(pattern_index=0, ax=None, add_label=True, show_plt=True)

Plot purity RB data of a single pattern.

property raw_data

Return raw data.

property rb_fit_fun

Return the fit function rb_fit_fun.

property rbfit_pur

Return the purity RB fitter.

property results

Return all the results.

property seeds

Return the number of loaded seeds.

property ydata

Return ydata (means and std devs).