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.

TomographyExperiment¶

class TomographyExperiment(circuit, backend=None, physical_qubits=None, measurement_basis=None, measurement_indices=None, preparation_basis=None, preparation_indices=None, conditional_circuit_clbits=False, basis_indices=None, analysis='default')[source]¶

Base experiment for quantum state and process tomography.

Analysis class reference

TomographyAnalysis

Experiment options

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

Options

Defined in the class TomographyExperiment:
- basis_indices (Iterable[Tuple[List[int], List[int]]])
  
  Default value: None
  
  The basis elements to be measured. If None All basis elements will be measured.
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 tomography experiment.

Parameters:

circuit (QuantumCircuit | Instruction | BaseOperator) – the quantum process circuit. If not a quantum circuit it must be a class that can be appended to a quantum circuit.
backend (Backend | None) – The backend to run the experiment on.
physical_qubits (Sequence[int] | None) – Optional, the physical qubits for the initial state circuit. If None this will be qubits [0, N) for an N-qubit circuit.
measurement_basis (MeasurementBasis | None) – Tomography basis for measurements. If set to None no tomography measurements will be performed.
measurement_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be measured as specified by the measurement_basis. If None all circuit physical qubits will be measured.
preparation_basis (PreparationBasis | None) – Tomography basis for measurements. If set to None no tomography preparations will be performed.
preparation_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be prepared as specified by the preparation_basis. If None all circuit physical qubits will be prepared.
basis_indices (Iterable[Tuple[List[int], List[int]]] | None) – Optional, the basis elements to be measured. If None All basis elements will be measured.
conditional_circuit_clbits (bool | Sequence[int] | Sequence[Clbit]) – Specify any clbits in the input circuit to treat as conditioning bits for conditional tomography. If set to True all circuit clbits will be treated as conditional. If False all circuit clbits will be marginalized over (Default: False).
analysis (BaseAnalysis | None | str) – Optional, a custom analysis instance to use. If "default" TomographyAnalysis will be used. If None no analysis instance will be set.

Raises:

QiskitError – If input params are invalid.

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]¶

Return a list of experiment circuits.

Returns:: A list of 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()¶

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