Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

QuantumVolumeAnalysis

class QuantumVolumeAnalysis

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 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.

• 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

QuantumVolumeAnalysis.options

Return the analysis options for run() method.

Methods

QuantumVolumeAnalysis.config()	Return the config dataclass for this analysis
QuantumVolumeAnalysis.copy()	Return a copy of the analysis
QuantumVolumeAnalysis.from_config(config)	Initialize an analysis class from analysis config
QuantumVolumeAnalysis.run(experiment_data[, ...])	Run analysis and update ExperimentData with analysis result.
QuantumVolumeAnalysis.set_options(**fields)	Set the analysis options for run() method.