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 (eitherAerSimulator
orStatevector
).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
Analysis class reference
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 initializenumpy.random.default_rng
when generating circuits. Thedefault_rng
will be initialized with this seed value every timecircuits()
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.
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. Thedefault_rng
will be initialized with this seed value every timecircuits()
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.
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:
- copy()¶
Return a copy of the experiment
- Return type:
- classmethod from_config(config)¶
Initialize an experiment from experiment config
- Return type:
- 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 experimentsanalysis()
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:
- 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
See also
The Setting options for your experiment guide for code example.
- set_transpile_options(**fields)¶
Set the transpiler options for
run()
method.- Parameters:
fields – The fields to update the options
- Raises:
QiskitError – If initial_layout is one of the fields.
See also
The Setting options for your experiment guide for code example.