BaseExperiment#

class BaseExperiment(physical_qubits, analysis=None, backend=None, experiment_type=None)[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.

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, analysis='default', timeout=None, **run_options)[source]#

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)[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

See also

The Setting options for your experiment guide for code example.

set_transpile_options(**fields)[source]#

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.