StandardRB#

class StandardRB(physical_qubits, lengths, backend=None, num_samples=3, seed=None, full_sampling=False)[source]#

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)

User manual

Randomized Benchmarking

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 every time 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.
- clifford_synthesis_method (str)
  
  Default value: "rb_default"
  
  The name of the Clifford synthesis plugin to use for building circuits of RB 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 (Backend | None) – The backend to run the experiment on.
num_samples (int) – Number of samples to generate for each sequence length.
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.
full_sampling (bool | None) – 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

analysis: BaseAnalysis#: 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 RB 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

enable_restless(rep_delay=None, override_processor_by_restless=True, suppress_t1_error=False)#

Enables a restless experiment by setting the restless run options and the restless data processor.

Parameters:

rep_delay (float | None) – The repetition delay. This is the delay between a measurement and the subsequent quantum circuit. Since the backends have dynamic repetition rates, the repetition delay can be set to a small value which is required for restless experiments. Typical values are 1 us or less.
override_processor_by_restless (bool) – If False, a data processor that is specified in the analysis options of the experiment is not overridden by the restless data processor. The default is True.
suppress_t1_error (bool) – If True, the default is False, then no error will be raised when rep_delay is larger than the T1 times of the qubits. Instead, a warning will be logged as restless measurements may have a large amount of noise.

Raises:

DataProcessorError – If the attribute rep_delay_range is not defined for the backend.
DataProcessorError – If a data processor has already been set but override_processor_by_restless is True.
DataProcessorError – If the experiment analysis does not have the data_processor option.
DataProcessorError – If the rep_delay is equal to or greater than the T1 time of one of the physical qubits in the experiment and the flag ignore_t1_check is False.

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, analysis='default', timeout=None, **run_options)#

Run an experiment and perform analysis.

Parameters:

backend (Backend | None) – Optional, the backend to run the experiment on. This will override any currently set backends for the single execution.
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.
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