QuantumVolume

class QuantumVolume(qubits, backend=None, trials=100, seed=None, simulation_backend=None)

Quantum Volume Experiment class.

Overview

Quantum Volume (QV) is a single-number metric that can be measured using a concrete protocol on near-term quantum computers of modest size. The QV method quantifies the largest random circuit of equal width and depth that the computer successfully implements. Quantum computing systems with high-fidelity operations, high connectivity, large calibrated gate sets, and circuit rewriting toolchains are expected to have higher quantum volumes.

The Quantum Volume is determined by the largest circuit depth \(d_{max}\), and equals to \(2^{d_{max}}\). See Qiskit Textbook for an explanation on the QV protocol.

In the QV experiment we generate QuantumVolume circuits on \(d\) qubits, which contain \(d\) layers, where each layer consists of random 2-qubit unitary gates from \(SU(4)\), followed by a random permutation on the \(d\) qubits. Then these circuits run on the quantum backend and on an ideal simulator (either AerSimulator or Statevector).

A depth \(d\) QV circuit 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.

See QuantumVolumeAnalysis documentation for additional information on QV experiment analysis.

References

[1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, Jay M. Gambetta, Validating quantum computers using randomized model circuits, Phys. Rev. A 100, 032328 (2019), doi: 10.1103/PhysRevA.100.032328 (open)

[2] Petar Jurcevic, Ali Javadi-Abhari, Lev S. Bishop, Isaac Lauer, Daniela F. Bogorin, Markus Brink, Lauren Capelluto, Oktay Günlük, Toshinari Itoko, Naoki Kanazawa, Abhinav Kandala, George A. Keefe, Kevin Krsulich, William Landers, Eric P. Lewandowski, Douglas T. McClure, Giacomo Nannicini, Adinath Narasgond, Hasan M. Nayfeh, Emily Pritchett, Mary Beth Rothwell, Srikanth Srinivasan, Neereja Sundaresan, Cindy Wang, Ken X. Wei, Christopher J. Wood, Jeng-Bang Yau, Eric J. Zhang, Oliver E. Dial, Jerry M. Chow, Jay M. Gambetta, Demonstration of quantum volume 64 on a superconducting quantum computing system (open)

Analysis Class Reference

QuantumVolumeAnalysis

Experiment Options

These options can be set by set_experiment_options() method.

Parameters

• trials (int) – Optional, number of times to generate new Quantum Volume circuits and calculate their heavy output.

• seed (None or int or SeedSequence or BitGenerator or Generator) – A seed used to initialize numpy.random.default_rng when generating circuits. The default_rng will be initialized with this seed value everytime circuits() is called.

Default values

Following values are set by default.

trials                    := 100
seed                      := None

Transpiler Options

This option can be set by set_transpile_options() method.

This option is used for circuit optimization. See the documentation of qiskit.transpile for available options.

Default values

Following values are set by default.

optimization_level        := 0

Backend Run Options

This option can be set by set_run_options() method.

This option is used for controlling job execution condition. Note that this option is provider dependent. See provider’s backend runner API for available options. See the documentation of IBMQBackend.run for the IBM Quantum Service.

Default values

Following values are set by default.

meas_level                := <MeasLevel.CLASSIFIED: 2>

Initialization

Initialize a quantum volume experiment.

Parameters

• qubits (Sequence[int]) – list of physical qubits for the experiment.

• backend (Optional[Backend]) – Optional, the backend to run the experiment on.

• trials (Optional[int]) – The number of trials to run the quantum volume circuit.

• seed (Union[int, SeedSequence, BitGenerator, Generator, None]) – Optional, seed used to initialize numpy.random.default_rng when generating circuits. The default_rng will be initialized with this seed value everytime circuits() is called.

• simulation_backend (Optional[Backend]) – The simulator backend to use to generate the expected results. the simulator must have a ‘save_probabilities’ method. If None AerSimulator simulator will be used (in case AerSimulator is not installed qiskit.quantum_info.Statevector will be used).

Attributes

QuantumVolume.analysis	Return the analysis instance for the experiment
QuantumVolume.analysis_options	Return the analysis options for run() analysis.
QuantumVolume.backend	Return the backend for the experiment
QuantumVolume.experiment_options	Return the options for the experiment.
QuantumVolume.experiment_type	Return experiment type.
QuantumVolume.num_qubits	Return the number of qubits for the experiment.
QuantumVolume.physical_qubits	Return the device qubits for the experiment.
QuantumVolume.run_options	Return options values for the experiment run() method.
QuantumVolume.transpile_options	Return the transpiler options for the run() method.

Methods

QuantumVolume.circuits()	Return a list of Quantum Volume circuits.
QuantumVolume.config()	Return the config dataclass for this experiment
QuantumVolume.copy()	Return a copy of the experiment
QuantumVolume.from_config(config)	Initialize an experiment from experiment config
QuantumVolume.run([backend, analysis, timeout])	Run an experiment and perform analysis.
QuantumVolume.run_analysis(experiment_data)	Run analysis and update ExperimentData with analysis result.
QuantumVolume.set_analysis_options(**fields)	Set the analysis options for run() method.
QuantumVolume.set_experiment_options(**fields)	Set the experiment options.
QuantumVolume.set_run_options(**fields)	Set options values for the experiment run() method.
QuantumVolume.set_transpile_options(**fields)	Set the transpiler options for run() method.