Source code for qiskit_experiments.library.characterization.correlated_readout_error

# This code is part of Qiskit.
#
# (C) Copyright IBM 2021, 2022.
#
# 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
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Correlated readout error calibration experiment class.
"""
from typing import Iterable, List, Optional
from qiskit import QuantumCircuit
from qiskit.providers.backend import BackendV2, Backend
from qiskit.exceptions import QiskitError
from qiskit_experiments.framework import BaseExperiment
from qiskit_experiments.library.characterization.analysis.correlated_readout_error_analysis import (
    CorrelatedReadoutErrorAnalysis,
)


def calibration_circuit(num_qubits: int, state_label: str) -> QuantumCircuit:
    """Return a calibration circuit.

    This is an N-qubit circuit where N is the length of the label.
    The circuit consists of X-gates on qubits with label bits equal to 1,
    and measurements of all qubits.
    """
    circ = QuantumCircuit(num_qubits, name="meas_mit_cal_" + state_label)
    for i, val in enumerate(reversed(state_label)):
        if val == "1":
            circ.x(i)
    circ.measure_all()
    circ.metadata = {"state_label": state_label}
    return circ




class CorrelatedReadoutError(BaseExperiment):
    r"""Correlated readout error characterization experiment.

    # section: overview
        This class constructs the a :class:`~qiskit.result.CorrelatedReadoutMitigator`
        containing the full assignment matrix :math:`A` characterizing the readout error
        for the given qubits from the experiment results accessible via the
        :meth:`~qiskit.result.CorrelatedReadoutMitigator.assignment_matrix` method.

        Readout errors affect quantum computation during the measurement
        of the qubits in a quantum device. By characterizing the readout errors,
        it is possible to construct a *readout error mitigator* that is used both
        to obtain a more accurate distribution of the outputs, and more accurate
        measurements of expectation value for measurables.

        The readout mitigator is generated from an *assignment matrix*:
        a :math:`2^n\times 2^n` matrix :math:`A` such that :math:`A_{y,x}` is the probability
        to observe :math:`y` given the true outcome should be :math:`x`. The assignment matrix is used
        to compute the *mitigation matrix* used in the readout error mitigation process itself.

        A *Correlated readout mitigator* uses the full :math:`2^n \times 2^n` assignment matrix, meaning
        it can only be used for small values of :math:`n`.
        The corresponding class in Qiskit is the
        :class:`~qiskit.result.CorrelatedReadoutMitigator` in :mod:`qiskit.result`.

        The experiment generates :math:`2^n` circuits, for every possible
        :math:`n`-qubit quantum state and constructs
        the assignment matrix and correlated mitigator from the results.

        See :class:`CorrelatedReadoutErrorAnalysis`
        documentation for additional information on correlated readout error experiment analysis.

    # section: analysis_ref
        :class:`CorrelatedReadoutErrorAnalysis`

    # section: example
        .. jupyter-execute::
            :hide-code:

            # Temporary workaround for missing support in Qiskit and qiskit-ibm-runtime
            from qiskit_experiments.test.patching import patch_sampler_test_support
            patch_sampler_test_support()

            from qiskit.providers.fake_provider import GenericBackendV2
            from qiskit_aer import AerSimulator

            num_qubits=5
            backend = AerSimulator.from_backend(GenericBackendV2(num_qubits=num_qubits,
                                                                 calibrate_instructions=True))

        .. jupyter-execute::

            from qiskit_experiments.library import CorrelatedReadoutError

            exp = CorrelatedReadoutError(physical_qubits=(0,1,2), backend=backend)

            exp_data = exp.run().block_for_results()
            display(exp_data.figure(0))
            exp_data.analysis_results(dataframe=True)

    # section: manual
        :doc:`/manuals/measurement/readout_mitigation`

    # section: reference
        .. ref_arxiv:: 1 2006.14044
    """

    def __init__(
        self,
        physical_qubits: Optional[Iterable[int]] = None,
        backend: Optional[Backend] = None,
    ):
        """Initialize a correlated readout error characterization experiment.

        Args:
            physical_qubits: Optional, the backend qubits being characterized
                for readout error. If None all qubits on the provided backend
                will be characterized.
            backend: Optional, the backend to characterize.

        Raises:
            QiskitError: If args are not valid.
        """
        if physical_qubits is None:
            if backend is None:
                raise QiskitError("`physical_qubits` and `backend` kwargs cannot both be None.")
            num_qubits = 0
            if isinstance(backend, BackendV2):
                num_qubits = backend.target.num_qubits
            elif isinstance(backend, Backend):
                num_qubits = backend.configuration().num_qubits
            if num_qubits:
                physical_qubits = range(num_qubits)
            else:
                raise QiskitError(f"Cannot infer backend qubits from backend {backend}")
        super().__init__(physical_qubits, backend=backend)
        self.analysis = CorrelatedReadoutErrorAnalysis()



    def circuits(self) -> List[QuantumCircuit]:
        """Returns the experiment's circuits"""
        labels = [bin(j)[2:].zfill(self.num_qubits) for j in range(2**self.num_qubits)]
        return [calibration_circuit(self.num_qubits, label) for label in labels]