RBFitter
class RBFitter(backend_result, cliff_lengths, rb_pattern=None)
Bases: qiskit.ignis.verification.randomized_benchmarking.fitters.RBFitterBase
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.
Methods
add_data
RBFitter.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
RBFitter.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
RBFitter.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].
fit_data
RBFitter.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
RBFitter.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
RBFitter.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.
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).