BaseExperiment¶

class BaseExperiment(physical_qubits, analysis=None, backend=None, experiment_type=None, backend_run=False)[source]¶

Abstract base class for experiments.

Initialize the experiment object.

Parameters:

physical_qubits (Sequence[int]) – list of physical qubits for the experiment.
analysis (BaseAnalysis | None) – Optional, the analysis to use for the experiment.
backend (Backend | None) – Optional, the backend to run the experiment on.
experiment_type (str | None) – Optional, the experiment type string.
backend_run (Options[bool]) – Optional, use backend run vs the sampler (temporary)

Raises:

QiskitError – If qubits contains duplicates.

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

abstract circuits()[source]¶

Return a list of experiment circuits.

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

Note

These circuits should be on qubits [0, .., N-1] for an N-qubit experiment. The circuits mapped to physical qubits are obtained via the internal _transpiled_circuits() method.

config()[source]¶

Return the config dataclass for this experiment

Return type:: ExperimentConfig

copy()[source]¶

Return a copy of the experiment

Return type:: BaseExperiment

classmethod from_config(config)[source]¶

Initialize an experiment from experiment config

Return type:: BaseExperiment

job_info(backend=None)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

Set options values for the experiment run() method.

Parameters:: fields – The fields to update the options