QuantumVolumeAnalysis

class QuantumVolumeAnalysis[source]

A class to analyze quantum volume experiments.

Overview

Calculate the quantum volume of the analysed system. The quantum volume is determined by the largest successful circuit depth. A depth is successful if it has mean heavy-output probability > 2/3 with confidence level > 0.977 (corresponding to z_value = 2), and at least 100 trials have been ran. we assume the error (standard deviation) of the heavy output probability is due to a binomial distribution. The standard deviation for binomial distribution is \(\sqrt{(np(1-p))}\), where \(n\) is the number of trials and \(p\) is the success probability.

Analysis options

These are the keyword arguments of the run() method.

Options

  • Defined in the class QuantumVolumeAnalysis:

    • plot (bool)

      Default value: True
      Set True to create figure for fit result.

    • ax (AxesSubplot)

      Default value: None
      Optional. A matplotlib axis object to draw.

    • plotter (BasePlotter)

      Default value: Instance of QuantumVolumePlotter
      Plotter object to use for figure generation.

  • Defined in the class BaseAnalysis:

    • figure_names (str or List[str])

      Default value: None
      Identifier of figures that appear in the experiment data to sort figures by name.

Initialization

Initialize the analysis object.

Attributes

options

Return the analysis options for run() method.

Methods

config()

Return the config dataclass for this analysis

Return type:

AnalysisConfig

copy()

Return a copy of the analysis

Return type:

BaseAnalysis

classmethod from_config(config)

Initialize an analysis class from analysis config

Return type:

BaseAnalysis

run(experiment_data, replace_results=False, **options)

Run analysis and update ExperimentData with analysis result.

Parameters:

  • experiment_data (ExperimentData) – the experiment data to analyze.

  • replace_results (bool) – If True clear any existing analysis results, figures, and artifacts in the experiment data and replace with new results. See note for additional information.

  • options – additional analysis options. See class documentation for supported options.

Returns:

An experiment data object containing analysis results, figures, and artifacts.

Raises:

QiskitError – If experiment_data container is not valid for analysis.

Return type:

ExperimentData

Note

Updating Results

If analysis is run with replace_results=True then any analysis results, figures, and artifacts in the experiment data will be cleared and replaced with the new analysis results. Saving this experiment data will replace any previously saved data in a database service using the same experiment ID.

If analysis is run with replace_results=False and the experiment data being analyzed has already been saved to a database service, or already contains analysis results or figures, a copy with a unique experiment ID will be returned containing only the new analysis results and figures. This data can then be saved as its own experiment to a database service.

set_options(**fields)

Set the analysis options for run() method.

Parameters:

fields – The fields to update the options