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RBFitter

RBFitter(backend_result, cliff_lengths, rb_pattern=None) GitHub(opens in a new tab)

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.

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).


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.

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