CorrelatedReadoutError¶

class CorrelatedReadoutError(physical_qubits=None, backend=None)[source]¶

Correlated readout error characterization experiment.

Overview

This class constructs the a CorrelatedReadoutMitigator containing the full assignment matrix \(A\) characterizing the readout error for the given qubits from the experiment results accessible via the assignment_matrix() method.

Readout errors affect quantum computation during the measurement of the qubits in a quantum device. By characterizing the readout errors, it is possible to construct a readout error mitigator that is used both to obtain a more accurate distribution of the outputs, and more accurate measurements of expectation value for measurables.

The readout mitigator is generated from an assignment matrix: a \(2^n\times 2^n\) matrix \(A\) such that \(A_{y,x}\) is the probability to observe \(y\) given the true outcome should be \(x\). The assignment matrix is used to compute the mitigation matrix used in the readout error mitigation process itself.

A Correlated readout mitigator uses the full \(2^n \times 2^n\) assignment matrix, meaning it can only be used for small values of \(n\). The corresponding class in Qiskit is the CorrelatedReadoutMitigator in qiskit.result.

The experiment generates \(2^n\) circuits, for every possible \(n\)-qubit quantum state and constructs the assignment matrix and correlated mitigator from the results.

See CorrelatedReadoutErrorAnalysis documentation for additional information on correlated readout error experiment analysis.

References

[1] Sergey Bravyi, Sarah Sheldon, Abhinav Kandala, David C. Mckay, Jay M. Gambetta, Mitigating measurement errors in multi-qubit experiments, Phys. Rev. A 103, 042605 (2021), doi: 10.1103/PhysRevA.103.042605 (open)

User manual

Readout Mitigation

Analysis class reference

CorrelatedReadoutErrorAnalysis

Experiment options

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

Options

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.library import CorrelatedReadoutError

exp = CorrelatedReadoutError(physical_qubits=(0,1,2), backend=backend)

exp_data = exp.run().block_for_results()
display(exp_data.figure(0))
exp_data.analysis_results(dataframe=True)

../_images/qiskit_experiments.library.characterization.CorrelatedReadoutError_1_0.png

	name	experiment	components	value	quality	backend	run_time
80f21790	Correlated Readout Mitigator	CorrelatedReadoutError	[Q0, Q1, Q2]	<qiskit_experiments.data_processing.mitigation...	None	aer_simulator_from(generic_backend_5q)	None

Initialization

Initialize a correlated readout error characterization experiment.

Parameters:

physical_qubits (Iterable[int] | None) – Optional, the backend qubits being characterized for readout error. If None all qubits on the provided backend will be characterized.
backend (Backend | None) – Optional, the backend to characterize.

Raises:

QiskitError – If args are not valid.

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]¶

Returns the experiment’s circuits

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