qiskit.ignis.verification.RBFitter

class RBFitter(backend_result, cliff_lengths, rb_pattern=None)

Class for fitters for randomized benchmarking.

Parameters

• backend_result (Result) – list of results (qiskit.Result).

• 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__(backend_result, cliff_lengths, rb_pattern=None)

Parameters

• backend_result (Result) – list of results (qiskit.Result).

• 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

__init__(backend_result, cliff_lengths[, …])	param backend_result list of results (qiskit.Result).
add_data(new_backend_result[, rerun_fit])	Add a new result.
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 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 randomized benchmarking data of a single pattern.

Attributes

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

add_data(new_backend_result, rerun_fit=True)

Add a new result. Re calculate the raw data, means and fit.

Parameters

• new_backend_result (list) – list of RB results.

• 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.

calc_data()

Retrieve probabilities of success from execution results.

Outputs results into an internal variable _raw_data which is a 3-dimensional list, where item (i,j,k) is the probability to measure the ground state for 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 fit.

fit_data()

Fit the RB results to an exponential curve.

Fit each of the patterns. 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.

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 data pattern 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.

• epc - error per Clifford.

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

Plot randomized benchmarking data of a single pattern.

Parameters

• pattern_index (int) – which RB pattern to plot.

• ax (Axes) – plot axis (if passed in).

• add_label (bool) – Add an EPC label.

• show_plt (bool) – display the plot.

Raises

ImportError – if matplotlib is not installed.

property raw_data

Return raw data.

property rb_fit_fun

Return the fit function rb_fit_fun.

property results

Return all the results.

property seeds

Return the number of loaded seeds.

property ydata

Return ydata (means and std devs).