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.

MultiStateDiscrimination

class MultiStateDiscrimination(physical_qubits, backend=None, n_states=None, schedules=None)[source]

An experiment that discriminates between the first \(n\) energy states.

Overview

The experiment creates \(n\) circuits that prepare, respectively, the energy states \(|0\rangle,\cdots,|n-1\rangle\). For, e.g., \(n=4\) the circuits are of the form

Circuit preparing \(|0\rangle\)

           ░ ┌─┐
       q: ─░─┤M├
           ░ └╥┘
    meas: ════╩═

...

Circuit preparing \(|3\rangle\)

          ┌───┐┌─────┐┌─────┐ ░ ┌─┐
       q: ┤ X ├┤ x12 ├┤ x23 ├─░─┤M├
          └───┘└─────┘└─────┘ ░ └╥┘
    meas: ═══════════════════════╩═

References

Qiskit Textbook

Analysis class reference

MultiStateDiscriminationAnalysis

Experiment options

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

Options
  • Defined in the class MultiStateDiscrimination:

    • n_states (int)

      Default value: 2
      The number of states to discriminate.
    • schedules (dict)

      Default value: None
      A dictionary of the schedules for the gates in the experiment. Each key is a gate name of the form xii+1 which should implement an x-rotation between level i and i+1.
  • 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.characterization import MultiStateDiscrimination

exp=MultiStateDiscrimination((0,), 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.MultiStateDiscrimination_1_0.png
name experiment components value quality backend run_time
2f796ed5 discriminator_config MultiStateDiscrimination [Q0] {'params': {'priors': None, 'reg_param': 0.0, ... None PulseBackendV2 None
5c2c4f48 fidelity MultiStateDiscrimination [Q0] 0.989746 None PulseBackendV2 None

Initialization

Setup an experiment to prepare different energy states on a given qubit.

Parameters:
  • physical_qubits (Sequence[int]) – A single-element sequence containing the qubit on which to run the experiment.

  • backend (Backend | None) – Optional, the backend to run the experiment on.

  • n_states (int | None) – The number of energy levels to prepare.

  • schedules (Dict[str, ScheduleBlock] | None) – The schedules of the x gates between neighboring energy levels.

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]

Create the circuits for the multi state discrimination experiment.

Returns:

A list of circuits preparing the different energy states.

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

See also

The Setting options for your experiment guide for code example.

set_transpile_options(**fields)

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.