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.

QuantumVolume¶

class QuantumVolume(physical_qubits, backend=None, trials=100, seed=None, simulation_backend=None)[source]¶

An experiment to measure the largest random square circuit that can be run on a processor.

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 the 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, Quantum Sci. Technol. 6 025020 (2021), doi: 10.1088/2058-9565/abe519 (open)

User manual

Quantum Volume

Analysis class reference

QuantumVolumeAnalysis

Experiment options

These options can be set by the set_experiment_options() method.

Options

Defined in the class QuantumVolume:
- trials (int)
  
  Default value: 100
  
  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)
  
  Default value: None
  
  A seed used to initialize numpy.random.default_rng when generating circuits. The default_rng will be initialized with this seed value every time circuits() is called.
Defined in the class BaseExperiment:
- max_circuits (Optional[int])
  
  Default value: None
  
  The maximum number of circuits per job when running an experiment on a backend.

Example

from qiskit_experiments.framework import BatchExperiment
from qiskit_experiments.library import QuantumVolume

qubits = tuple(range(4)) # Can use specific qubits. for example [2, 4, 7, 10]
qv_exp = QuantumVolume(qubits, seed=42)
qv_exp.set_transpile_options(optimization_level=3)
qv_exp.set_run_options(shots=1000)

expdata = qv_exp.run(backend=backend).block_for_results()
display(expdata.figure(0))

for result in expdata.analysis_results():
    print(result)

../_images/qiskit_experiments.library.quantum_volume.QuantumVolume_1_0.png

AnalysisResult
- name: mean_HOP
- value: 0.73+/-0.04
- quality: bad
- extra: <8 items>
- device_components: ['Q0', 'Q1', 'Q2', 'Q3']
- verified: False
AnalysisResult
- name: quantum_volume
- value: 1
- quality: bad
- extra: <8 items>
- device_components: ['Q0', 'Q1', 'Q2', 'Q3']
- verified: False

Initialization

Initialize a quantum volume experiment.

Parameters:

physical_qubits (Sequence[int]) – list of physical qubits for the experiment.
backend (Backend | None) – Optional, the backend to run the experiment on.
trials (int | None) – The number of trials to run the quantum volume circuit.
seed (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 every time circuits() is called.
simulation_backend (Backend | None) – The simulator backend to use to generate the expected results. the simulator must have a ‘save_probabilities’ method. If None, the qiskit_aer.AerSimulator simulator will be used (in case qiskit-aer is not installed, qiskit.quantum_info.Statevector will be used).

Attributes

analysis¶: Return the analysis instance for the experiment

backend¶: Return the backend for the experiment

experiment_options¶: Return the options for the experiment.

experiment_type¶: Return experiment type.

num_qubits¶: Return the number of qubits for the experiment.

physical_qubits¶: Return the device qubits for the experiment.

run_options¶: Return options values for the experiment run() method.

transpile_options¶: Return the transpiler options for the run() method.

Methods

circuits()[source]¶

Return a list of Quantum Volume circuits.

Returns:: A list of QuantumCircuit.
Return type:: List[QuantumCircuit]

config()¶

Return the config dataclass for this experiment

Return type:: ExperimentConfig

copy()¶

Return a copy of the experiment

Return type:: BaseExperiment

classmethod from_config(config)¶

Initialize an experiment from experiment config

Return type:: BaseExperiment

job_info(backend=None)¶

Get information about job distribution for the experiment on a specific backend.

Parameters:

backend (Backend) – Optional, the backend for which to get job distribution information. If not specified, the experiment must already have a set backend.

Returns:

A dictionary containing information about job distribution.

”Total number of circuits in the experiment”: Total number of circuits in the experiment.

”Maximum number of circuits per job”: Maximum number of circuits in one job based on backend and experiment settings.

”Total number of jobs”: Number of jobs needed to run this experiment on the currently set backend.

Return type:

dict

Raises:

QiskitError – if backend is not specified.

run(backend=None, sampler=None, analysis='default', timeout=None, backend_run=None, **run_options)¶

Run an experiment and perform analysis.

Parameters:

backend (Backend | None) – Optional, the backend to run on. Will override existing backend settings.
sampler (BaseSamplerV2 | None) – Optional, the sampler to run the experiment on. If None then a sampler will be invoked from previously set backend
analysis (BaseAnalysis | None) – Optional, a custom analysis instance to use for performing analysis. If None analysis will not be run. If "default" the experiments analysis() instance will be used if it contains one.
timeout (float | None) – Time to wait for experiment jobs to finish running before cancelling.
backend_run (bool | None) – Use backend run (temp option for testing)
run_options – backend runtime options used for circuit execution.

Returns:

The experiment data object.

Raises:

QiskitError – If experiment is run with an incompatible existing ExperimentData container.

Return type:

ExperimentData

set_experiment_options(**fields)¶

Set the experiment options.

Parameters:: fields – The fields to update the options
Raises:: AttributeError – If the field passed in is not a supported options

set_run_options(**fields)¶

Set options values for the experiment run() method.

Parameters:: fields – The fields to update the options