Source code for qiskit_experiments.library.tomography.mit_qpt_experiment

# This code is part of Qiskit.
#
# (C) Copyright IBM 2023.
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# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
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"""
Quantum Process Tomography experiment
"""

from typing import Union, Optional, Iterable, List, Tuple, Sequence
from qiskit.providers.backend import Backend
from qiskit.circuit import QuantumCircuit, Instruction, Clbit
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit_experiments.framework import BatchExperiment, BaseAnalysis
from qiskit_experiments.library.characterization.local_readout_error import LocalReadoutError
from .qpt_experiment import ProcessTomography
from .mit_tomography_analysis import MitigatedTomographyAnalysis
from . import basis


[docs] class MitigatedProcessTomography(BatchExperiment): """A batched experiment to characterize readout error then perform process tomography for doing readout error mitigated process tomography. # section: overview Readout error mitigated Quantum process tomography is a batch experiment consisting of a :class:`~.LocalReadoutError` characterization experiments, followed by a :class:`~.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. # section: note Performing readout error mitigation full process tomography on an `N`-qubit circuit requires running 2 readout error characterization circuits and :math:`4^N 3^N` measurement circuits using the Pauli preparation and measurement bases. # section: analysis_ref :py:class:`MitigatedTomographyAnalysis` # section: see_also * :py:class:`qiskit_experiments.library.tomography.ProcessTomography` * :py:class:`qiskit_experiments.library.characterization.LocalReadoutError` # section: example .. jupyter-execute:: :hide-code: # backend from qiskit_aer import AerSimulator from qiskit_ibm_runtime.fake_provider import FakePerth backend = AerSimulator.from_backend(FakePerth()) .. jupyter-execute:: 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")) """ def __init__( self, circuit: Union[QuantumCircuit, Instruction, BaseOperator], backend: Optional[Backend] = None, physical_qubits: Optional[Sequence[int]] = None, measurement_indices: Optional[Sequence[int]] = None, preparation_indices: Optional[Sequence[int]] = None, basis_indices: Optional[Iterable[Tuple[List[int], List[int]]]] = None, conditional_circuit_clbits: Union[bool, Sequence[int], Sequence[Clbit]] = False, analysis: Union[BaseAnalysis, None, str] = "default", ): """Initialize a quantum process tomography experiment. Args: circuit: the quantum process circuit. If not a quantum circuit it must be a class that can be appended to a quantum circuit. backend: The backend to run the experiment on. physical_qubits: Optional, the physical qubits for the initial state circuit. If None this will be qubits [0, N) for an N-qubit circuit. measurement_indices: Optional, the `physical_qubits` indices to be measured. If None all circuit physical qubits will be measured. preparation_indices: Optional, the `physical_qubits` indices to be prepared. If None all circuit physical qubits will be prepared. basis_indices: 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: 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: Optional, a custom tomography analysis instance to use. If ``"default"`` :class:`~.ProcessTomographyAnalysis` will be used. If None no analysis instance will be set. """ tomo_exp = ProcessTomography( circuit, backend=backend, physical_qubits=physical_qubits, measurement_basis=basis.PauliMeasurementBasis(), measurement_indices=measurement_indices, preparation_basis=basis.PauliPreparationBasis(), preparation_indices=preparation_indices, basis_indices=basis_indices, conditional_circuit_clbits=conditional_circuit_clbits, analysis=analysis, ) roerror_exp = LocalReadoutError( tomo_exp.physical_qubits, backend=backend, ) if analysis is None: mit_analysis = (None,) else: mit_analysis = MitigatedTomographyAnalysis(roerror_exp.analysis, tomo_exp.analysis) super().__init__( [roerror_exp, tomo_exp], backend=backend, flatten_results=True, analysis=mit_analysis )