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.

ProcessTomography

class ProcessTomography(circuit, backend=None, physical_qubits=None, measurement_basis=<PauliMeasurementBasis: PauliMeasurementBasis>, measurement_indices=None, preparation_basis=<PauliPreparationBasis: PauliPreparationBasis>, preparation_indices=None, basis_indices=None, conditional_circuit_clbits=False, analysis='default', target='default')

An experiment to reconstruct the quantum channel from measurement data.

Overview

Quantum process tomography (QPT) is a method for experimentally reconstructing the quantum channel from measurement data.

A QPT experiment prepares multiple input states, evolves them by the circuit, then performs multiple measurements in different measurement bases. The resulting measurement data is then post-processed by a tomography fitter to reconstruct the quantum channel.

Analysis class reference

ProcessTomographyAnalysis

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.

Example

import numpy as np
from qiskit import QuantumCircuit
from qiskit_experiments.library import ProcessTomography

num_qubits = 2
qc_ghz = QuantumCircuit(num_qubits)
qc_ghz.h(0)
qc_ghz.s(0)

for i in range(1, num_qubits):
    qc_ghz.cx(0, i)

qptexp = ProcessTomography(qc_ghz)
qptdata = qptexp.run(backend=backend,
                     shots=1000,
                     seed_simulator=100,).block_for_results()
choi_out = qptdata.analysis_results("state").value

# extracting a densitymatrix from choi_out
from qiskit.visualization import plot_state_city
import qiskit.quantum_info as qinfo

_rho_exp_00 = np.array([[None, None, None, None],
                        [None, None, None, None],
                        [None, None, None, None],
                        [None, None, None, None]])

for i in range(4):
    for j in range(4):
        _rho_exp_00[i][j] = choi_out.data[i][j]

rho_exp_00 = qinfo.DensityMatrix(_rho_exp_00)
display(plot_state_city(rho_exp_00, title="Density Matrix"))

Note

Performing full process tomography on an N-qubit circuit requires running \(4^N 3^N\) measurement circuits when using the default preparation and measurement bases.

See also

• Superclass qiskit_experiments.library.tomography.tomography_experiment.TomographyExperiment

Initialization

Initialize a quantum process 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) – Tomography basis for measurements. If not specified the default basis is the PauliMeasurementBasis.

• measurement_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be measured. If None all circuit physical qubits will be measured.

• preparation_basis (PreparationBasis) – Tomography basis for measurements. If not specified the default basis is the PauliPreparationBasis.

• preparation_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be prepared. If None all circuit physical qubits will be prepared.

• basis_indices (Sequence[Tuple[List[int], List[int]]] | None) – Optional, a list of basis indices for generating partial tomography measurement data. Each item should be given as a pair of lists of preparation and measurement basis configurations ([p[0], p[1], ...], [m[0], m[1], ...]), where p[i] is the preparation basis index, and m[i] is the measurement basis index for qubit-i. If not specified full tomography for all indices of the preparation and measurement bases will be performed.

• conditional_circuit_clbits (bool | Sequence[int] | Sequence[Clbit]) – Optional, the clbits in the source circuit to be conditioned on when reconstructing the channel. If True all circuit clbits will be conditioned on. Enabling this will return a list of reconstructed channel components conditional on the values of these clbit values.

• analysis (BaseAnalysis | None | str) – Optional, a custom analysis instance to use. If "default" ProcessTomographyAnalysis will be used. If None no analysis instance will be set.

• target (Statevector | DensityMatrix | None | str) – Optional, a custom quantum state target for computing the state fidelity of the fitted density matrix during analysis. If “default” the state will be inferred from the input circuit if it contains no classical instructions.

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()

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

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.