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.

StandardRB

class StandardRB(physical_qubits, lengths, backend=None, num_samples=3, seed=None, full_sampling=False)

An experiment to characterize the error rate of a gate set on a device.

Overview

Randomized Benchmarking (RB) is an efficient and robust method for estimating the average error rate of a set of quantum gate operations. See Qiskit Textbook for an explanation on the RB method.

A standard RB experiment generates sequences of random Cliffords such that the unitary computed by the sequences is the identity. After running the sequences on a backend, it calculates the probabilities to get back to the ground state, fits an exponentially decaying curve, and estimates the Error Per Clifford (EPC), as described in Refs. [1, 2].

Note

In 0.5.0, the default value of optimization_level in transpile_options changed from 0 to 1 for RB experiments. That may result in shorter RB circuits hence slower decay curves than before.

References

[1] Easwar Magesan, J. M. Gambetta, Joseph Emerson, Robust randomized benchmarking of quantum processes, Phys. Rev. Lett. 106, 180504 (2011), doi: 10.1103/PhysRevLett.106.180504 (open)

[2] Easwar Magesan, Jay M. Gambetta, Joseph Emerson, Characterizing Quantum Gates via Randomized Benchmarking, Phys. Rev. A 85, 042311 (2012), doi: 10.1103/PhysRevA.85.042311 (open)

Analysis class reference

RBAnalysis

Experiment options

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

Options

• Defined in the class StandardRB:

  • lengths (List[int])

    Default value: None

    A list of RB sequences lengths.

  • num_samples (int)

    Default value: None

    Number of samples to generate for each sequence length.

  • 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 everytime circuits() is called.

  • full_sampling (bool)

    Default value: None

    If True all Cliffords are independently sampled for all lengths. If False for sample of lengths longer sequences are constructed by appending additional Clifford samples to shorter sequences.

• 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 standard randomized benchmarking experiment.

Parameters:

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

• lengths (Iterable[int]) – A list of RB sequences lengths.

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

• num_samples (int) – Number of samples to generate for each sequence length.

• 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.

• full_sampling (Optional[bool]) – If True all Cliffords are independently sampled for all lengths. If False for sample of lengths longer sequences are constructed by appending additional samples to shorter sequences. The default is False.

Raises:

QiskitError – If any invalid argument is supplied.

Attributes

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

Methods

StandardRB.circuits()	Return a list of RB circuits.
StandardRB.config()	Return the config dataclass for this experiment
StandardRB.copy()	Return a copy of the experiment
StandardRB.enable_restless([rep_delay, ...])	Enables a restless experiment by setting the restless run options and the restless data processor.
StandardRB.from_config(config)	Initialize an experiment from experiment config
StandardRB.run([backend, analysis, timeout])	Run an experiment and perform analysis.
StandardRB.set_experiment_options(**fields)	Set the experiment options.
StandardRB.set_run_options(**fields)	Set options values for the experiment run() method.
StandardRB.set_transpile_options(**fields)	Set the transpiler options for run() method.