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.

MitigatedProcessTomography¶

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

A batched experiment to characterize readout error then perform process tomography for doing readout error mitigated process tomography.

Overview

Readout error mitigated Quantum process tomography is a batch experiment consisting of a LocalReadoutError characterization experiments, followed by a ProcessTomography experiment.

During analysis the assignment matrix local readout error model is used to automatically construct a noisy Pauli measurement basis for performing readout error mitigated process tomography fitting.

Analysis class reference

MitigatedTomographyAnalysis

Experiment options

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

Options

Defined in the class BatchExperiment:
- separate_jobs (Boolean)
  
  Default value: False
  
  Whether to route different sub-experiments to different jobs.
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 MitigatedProcessTomography

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)

mitqptexp = MitigatedProcessTomography(qc_ghz)
mitqptexp.set_run_options(shots=1000)
mitqptdata = mitqptexp.run(backend=backend,
                           seed_simulator=100,).block_for_results()
mitigated_choi_out = mitqptdata.analysis_results("state").value

# extracting a densitymatrix from mitigated_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] = mitigated_choi_out.data[i][j]

mitigated_rho_exp_00 = qinfo.DensityMatrix(_rho_exp_00)
display(plot_state_city(mitigated_rho_exp_00, title="mitigated Density Matrix"))

../_images/qiskit_experiments.library.tomography.MitigatedProcessTomography_1_0.png

Note

Performing readout error mitigation full process tomography on an N-qubit circuit requires running 2 readout error characterization circuits and \(4^N 3^N\) measurement circuits using the Pauli preparation and measurement bases.

See also

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_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be measured. If None all circuit physical qubits will be measured.
preparation_indices (Sequence[int] | None) – Optional, the physical_qubits indices to be prepared. If None all circuit physical qubits will be prepared.
basis_indices (Iterable[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 state. If True all circuit clbits will be conditioned on. Enabling this will return a list of reconstructed state components conditional on the values of these clbit values.
analysis (BaseAnalysis | None | str) – Optional, a custom tomography analysis instance to use. If "default" ProcessTomographyAnalysis will be used. If None no analysis instance will be set.

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_experiments¶: Return the number of sub experiments

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.

component_experiment(index=None)¶

Return the component Experiment object.

Parameters:: index (int) – Experiment index, or None if all experiments are to be returned.
Returns:: The component experiment(s).
Return type:: BaseExperiment

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