PurityRBFitter
class PurityRBFitter(purity_result, npurity, cliff_lengths, rb_pattern=None)
Bases: qiskit.ignis.verification.randomized_benchmarking.fitters.RBFitterBase
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.
Methods
F234
static PurityRBFitter.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
PurityRBFitter.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
PurityRBFitter.add_zdict_ops()
Creating all Z-correlators in order to compute the expectation values.
calc_data
PurityRBFitter.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
PurityRBFitter.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
PurityRBFitter.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
PurityRBFitter.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
PurityRBFitter.plot_rb_data(pattern_index=0, ax=None, add_label=True, show_plt=True)
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).