# qiskit.ignis.verification.QVFitter¶

class QVFitter(backend_result=None, statevector_result=None, qubit_lists=None)[소스]

Class for fitters for quantum volume.

매개변수
• backend_result (list) – list of results (qiskit.Result).

• statevector_result (list) – the ideal statevectors of each circuit

• qubit_lists (list) – list of qubit lists (what was passed to the circuit generation)

__init__(backend_result=None, statevector_result=None, qubit_lists=None)[소스]
매개변수
• backend_result (list) – list of results (qiskit.Result).

• statevector_result (list) – the ideal statevectors of each circuit

• qubit_lists (list) – list of qubit lists (what was passed to the circuit generation)

Methods

 __init__([backend_result, …]) param backend_result list of results (qiskit.Result). add_data(new_backend_result[, rerun_fit]) Add a new result. add_statevectors(new_statevector_result) Add the ideal results and convert to the heavy outputs. calc_confidence_level(z_value) Calculate confidence level using z value. Make a count dictionary for each unique circuit from all the results. Convert the heavy outputs in the different trials into mean and error for plotting. calc_z_value(mean, sigma) Calculate z value using mean and sigma. plot_hop_accumulative(depth[, ax, figsize]) Plot individual and accumulative heavy output probability (HOP) as a function of number of trials. plot_qv_data([ax, show_plt, figsize, …]) Plot the qv data as a function of depth plot_qv_trial(depth, trial_index[, figsize, ax]) Plot individual trial. Return the volume for each depth. Return whether each depth was successful (> 2/3 with confidence level > 0.977 corresponding to z_value = 2) and the confidence level.

Attributes

 depths Return depth list. heavy_output_counts Return the number of heavy output counts as measured. heavy_output_prob_ideal Return the heavy output probability ideally. heavy_outputs Return the ideal heavy outputs dictionary. qubit_lists Return depth list. results Return all the results. ydata Return the average and std of the output probability.
add_data(new_backend_result, rerun_fit=True)[소스]

Add a new result. Re calculate fit

매개변수
• new_backend_result (list) – list of qv results

• rerun_fit (bool) – re calculate the means and fit the result

예외

QiskitError – If the ideal distribution isn’t loaded yet

Assumes that ‘result’ was executed is the output of circuits generated by qv_circuits,

add_statevectors(new_statevector_result)[소스]

Add the ideal results and convert to the heavy outputs.

Assume the result is from ‘statevector_simulator’

매개변수

new_statevector_result (list) – ideal results

예외

QiskitError – If the result has already been added for the circuit

calc_confidence_level(z_value)[소스]

Calculate confidence level using z value.

Accumulative probability for standard normal distribution in [-z, +infinity] is 1/2 (1 + erf(z/sqrt(2))), where z = (X - mu)/sigma = (hmean - 2/3)/sigma

매개변수

z_value (float) – z value in in standard normal distibution.

반환값

confidence level in decimal (not percentage).

반환 형식

float

calc_data()[소스]

Make a count dictionary for each unique circuit from all the results.

Calculate the heavy output probability.

Assumes that ‘result’ was executed is the output of circuits generated by qv_circuits,

calc_statistics()[소스]

Convert the heavy outputs in the different trials into mean and error for plotting.

Here we assume the error is due to a binomial distribution. Error (standard deviation) for binomial distribution is sqrt(np(1-p)), where n is the number of trials (self._ntrials) and p is the success probability (self._ydata[0][depthidx]/self._ntrials).

calc_z_value(mean, sigma)[소스]

Calculate z value using mean and sigma.

매개변수
• mean (float) – mean

• sigma (float) – standard deviation

반환값

z_value in standard normal distibution.

반환 형식

float

property depths

Return depth list.

property heavy_output_counts

Return the number of heavy output counts as measured.

property heavy_output_prob_ideal

Return the heavy output probability ideally.

property heavy_outputs

Return the ideal heavy outputs dictionary.

plot_hop_accumulative(depth, ax=None, figsize=(7, 5))[소스]

Plot individual and accumulative heavy output probability (HOP) as a function of number of trials.

매개변수
• depth (int) – depth of QV circuits

• ax (Axes or None) – plot axis (if passed in).

• figsize (tuple) – figure size in inches.

예외

ImportError – If matplotlib is not installed.

반환값

A figure of individual and accumulative HOP as a function of number of trials, with 2-sigma confidence interval and 2/3 threshold.

반환 형식

matplotlib.Figure

plot_qv_data(ax=None, show_plt=True, figsize=(7, 5), set_title=True, title=None)[소스]

Plot the qv data as a function of depth

매개변수
• ax (Axes or None) – plot axis (if passed in).

• show_plt (bool) – display the plot.

• figsize (tuple) – Figure size in inches.

• set_title (bool) – set figure title.

• title (String or None) – text for setting figure title

예외

ImportError – If matplotlib is not installed.

반환값

A figure of Quantum Volume data (heavy output probability) with two-sigma error bar as a function of circuit depth.

반환 형식

matplotlib.Figure

plot_qv_trial(depth, trial_index, figsize=(7, 5), ax=None)[소스]

Plot individual trial. :param depth: circuit depth :type depth: int :param trial_index: trial index :type trial_index: int :param figsize: Figure size in inches. :type figsize: tuple :param ax: plot axis (if passed in). :type ax: Axes or None

반환값

A figure for histogram of ideal and experiment probabilities.

반환 형식

matplotlib.Figure

quantum_volume()[소스]

Return the volume for each depth.

반환값

List of quantum volumes

반환 형식

list

property qubit_lists

Return depth list.

qv_success()[소스]

Return whether each depth was successful (> 2/3 with confidence level > 0.977 corresponding to z_value = 2) and the confidence level.

반환값

List of list of 2 elements for each depth: - success True/False - confidence level

반환 형식

list

property results

Return all the results.

property ydata

Return the average and std of the output probability.