qiskit.ignis.verification.PurityRBFitter¶

class
PurityRBFitter
(purity_result, npurity, cliff_lengths, rb_pattern=None)[source]¶ 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.

__init__
(purity_result, npurity, cliff_lengths, rb_pattern=None)[source]¶  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
(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
__init__
(purity_result, npurity, cliff_lengths) param purity_result
list of results of the
add_data
(new_purity_result[, rerun_fit])Add a new result.
Creating all Zcorrelators in order to compute the expectation values.
Retrieve probabilities of success from execution results.
Extract averages and std dev from the raw data (self._raw_data).
fit_data
()Fit the Purity 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 purity RB data of a single pattern.
Attributes
Return clifford lengths.
Return the purity fit parameters.
Return raw data.
Return the fit function rb_fit_fun.
Return the purity RB fitter.
Return all the results.
Return the number of loaded seeds.
Return ydata (means and std devs).

static
F234
(n, a, b)[source]¶ 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
(new_purity_result, rerun_fit=True)[source]¶ Add a new result.
 Parameters
new_purity_result (list) – list of RB results of the purity RB circuits.
rerun_fit (bool) – recalculate 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.

calc_data
()[source]¶ Retrieve probabilities of success from execution results.
Measure the purity calculation into an internal variable _raw_data which is a 3dimensional 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
()[source]¶ 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 the purity fit parameters.

fit_data
()[source]¶ 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
(patt_ind, fit_guess)[source]¶ 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
(pattern_index=0, ax=None, add_label=True, show_plt=True)[source]¶ Plot purity RB data of a single pattern.

property
raw_data
¶ Return raw data.

property
rb_fit_fun
¶ Return the fit function rb_fit_fun.

property
rbfit_pur
¶ Return the purity RB fitter.

property
results
¶ Return all the results.

property
seeds
¶ Return the number of loaded seeds.

property
ydata
¶ Return ydata (means and std devs).