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