# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2023.
#
# 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.

"""Quantum Instance module"""

from typing import Optional, List, Union, Dict, Callable, Tuple
from enum import Enum
import copy
import logging
import time
import warnings

import numpy as np

from qiskit.qobj import Qobj
from qiskit.utils import circuit_utils
from qiskit.exceptions import QiskitError
from qiskit.utils.backend_utils import (
    is_ibmq_provider,
    is_statevector_backend,
    is_simulator_backend,
    is_local_backend,
    is_basicaer_provider,
    support_backend_options,
    _get_backend_provider,
    _get_backend_interface_version,
)
from qiskit.utils.mitigation import (
    CompleteMeasFitter,
    TensoredMeasFitter,
)
from qiskit.utils.deprecation import deprecate_func

logger = logging.getLogger(__name__)


class _MeasFitterType(Enum):
    """Meas Fitter Type."""

    COMPLETE_MEAS_FITTER = 0
    TENSORED_MEAS_FITTER = 1

    @staticmethod
    def type_from_class(meas_class):
        """
        Returns fitter type from class
        """
        if meas_class == CompleteMeasFitter:
            return _MeasFitterType.COMPLETE_MEAS_FITTER
        elif meas_class == TensoredMeasFitter:
            return _MeasFitterType.TENSORED_MEAS_FITTER
        try:
            from qiskit.ignis.mitigation.measurement import (
                CompleteMeasFitter as CompleteMeasFitter_IG,
                TensoredMeasFitter as TensoredMeasFitter_IG,
            )
        except ImportError:
            pass
        if meas_class == CompleteMeasFitter_IG:
            warnings.warn(
                "The use of qiskit-ignis for measurement mitigation is "
                "deprecated and will be removed in a future release. Instead "
                "use the CompleteMeasFitter class from qiskit.utils.mitigation",
                DeprecationWarning,
                stacklevel=3,
            )
            return _MeasFitterType.COMPLETE_MEAS_FITTER
        elif meas_class == TensoredMeasFitter_IG:
            warnings.warn(
                "The use of qiskit-ignis for measurement mitigation is "
                "deprecated and will be removed in a future release. Instead "
                "use the TensoredMeasFitter class from qiskit.utils.mitigation",
                DeprecationWarning,
                stacklevel=3,
            )
            return _MeasFitterType.TENSORED_MEAS_FITTER
        else:
            raise QiskitError(f"Unknown fitter {meas_class}")

    @staticmethod
    def type_from_instance(meas_instance):
        """
        Returns fitter type from instance
        """
        if isinstance(meas_instance, CompleteMeasFitter):
            return _MeasFitterType.COMPLETE_MEAS_FITTER
        elif isinstance(meas_instance, TensoredMeasFitter):
            return _MeasFitterType.TENSORED_MEAS_FITTER
        try:
            from qiskit.ignis.mitigation.measurement import (
                CompleteMeasFitter as CompleteMeasFitter_IG,
                TensoredMeasFitter as TensoredMeasFitter_IG,
            )
        except ImportError:
            pass
        if isinstance(meas_instance, CompleteMeasFitter_IG):
            warnings.warn(
                "The use of qiskit-ignis for measurement mitigation is "
                "deprecated and will be removed in a future release. Instead "
                "use the CompleteMeasFitter class from qiskit.utils.mitigation",
                DeprecationWarning,
                stacklevel=3,
            )
            return _MeasFitterType.COMPLETE_MEAS_FITTER
        elif isinstance(meas_instance, TensoredMeasFitter_IG):
            warnings.warn(
                "The use of qiskit-ignis for measurement mitigation is "
                "deprecated and will be removed in a future release. Instead "
                "use the TensoredMeasFitter class from qiskit.utils.mitigation",
                DeprecationWarning,
                stacklevel=3,
            )
            return _MeasFitterType.TENSORED_MEAS_FITTER
        else:
            raise QiskitError(f"Unknown fitter {meas_instance}")


class QuantumInstance:
    """Deprecated: Quantum Backend including execution setting."""

    _BACKEND_CONFIG = ["basis_gates", "coupling_map"]
    _COMPILE_CONFIG = ["initial_layout", "seed_transpiler", "optimization_level"]
    _RUN_CONFIG = ["shots", "memory", "seed_simulator"]
    _QJOB_CONFIG = ["timeout", "wait"]
    _NOISE_CONFIG = ["noise_model"]

    # https://github.com/Qiskit/qiskit-aer/blob/master/qiskit/providers/aer/backends/qasm_simulator.py
    _BACKEND_OPTIONS_QASM_ONLY = ["statevector_sample_measure_opt", "max_parallel_shots"]
    _BACKEND_OPTIONS = [
        "initial_statevector",
        "chop_threshold",
        "max_parallel_threads",
        "max_parallel_experiments",
        "statevector_parallel_threshold",
        "statevector_hpc_gate_opt",
    ] + _BACKEND_OPTIONS_QASM_ONLY

    @deprecate_func(
        since="0.24.0",
        additional_msg="For code migration guidelines, visit https://qisk.it/qi_migration.",
    )
    def __init__(
        self,
        backend,
        # run config
        shots: Optional[int] = None,
        seed_simulator: Optional[int] = None,
        # backend properties
        basis_gates: Optional[List[str]] = None,
        coupling_map=None,
        # transpile
        initial_layout=None,
        pass_manager=None,
        bound_pass_manager=None,
        seed_transpiler: Optional[int] = None,
        optimization_level: Optional[int] = None,
        # simulation
        backend_options: Optional[Dict] = None,
        noise_model=None,
        # job
        timeout: Optional[float] = None,
        wait: float = 5.0,
        # others
        skip_qobj_validation: bool = True,
        measurement_error_mitigation_cls: Optional[Callable] = None,
        cals_matrix_refresh_period: int = 30,
        measurement_error_mitigation_shots: Optional[int] = None,
        job_callback: Optional[Callable] = None,
        mit_pattern: Optional[List[List[int]]] = None,
        max_job_retries: int = 50,
    ) -> None:
        """
        Quantum Instance holds a Qiskit Terra backend as well as configuration for circuit
        transpilation and execution. When provided to an Aqua algorithm the algorithm will
        execute the circuits it needs to run using the instance.

        Args:
            backend (Backend): Instance of selected backend
            shots: Number of repetitions of each circuit, for sampling. If None, the shots are
                extracted from the backend. If the backend has none set, the default is 1024.
            seed_simulator: Random seed for simulators
            basis_gates: List of basis gate names supported by the
                target. Defaults to basis gates of the backend.
            coupling_map (Optional[Union['CouplingMap', List[List]]]):
                Coupling map (perhaps custom) to target in mapping
            initial_layout (Optional[Union['Layout', Dict, List]]):
                Initial layout of qubits in mapping
            pass_manager (Optional['PassManager']): Pass manager to handle how to compile the circuits.
                To run only this pass manager and not the ``bound_pass_manager``, call the
                :meth:`~qiskit.utils.QuantumInstance.transpile` method with the argument
                ``pass_manager=quantum_instance.unbound_pass_manager``.
            bound_pass_manager (Optional['PassManager']): A second pass manager to apply on bound
                circuits only, that is, circuits without any free parameters. To only run this pass
                manager and not ``pass_manager`` call the
                :meth:`~qiskit.utils.QuantumInstance.transpile` method with the argument
                ``pass_manager=quantum_instance.bound_pass_manager``.
                manager should also be run.
            seed_transpiler: The random seed for circuit mapper
            optimization_level: How much optimization to perform on the circuits.
                Higher levels generate more optimized circuits, at the expense of longer
                transpilation time.
            backend_options: All running options for backend, please refer
                to the provider of the backend for information as to what options it supports.
            noise_model (Optional['NoiseModel']): noise model for simulator
            timeout: Seconds to wait for job. If None, wait indefinitely.
            wait: Seconds between queries for job result
            skip_qobj_validation: Bypass Qobj validation to decrease circuit
                processing time during submission to backend.
            measurement_error_mitigation_cls: The approach to mitigate
                measurement errors. The classes :class:`~qiskit.utils.mitigation.CompleteMeasFitter`
                or :class:`~qiskit.utils.mitigation.TensoredMeasFitter` from the
                :mod:`qiskit.utils.mitigation` module can be used here as exact values, not
                instances. ``TensoredMeasFitter`` doesn't support the ``subset_fitter`` method.
            cals_matrix_refresh_period: How often to refresh the calibration
                matrix in measurement mitigation. in minutes
            measurement_error_mitigation_shots: The number of shots number for
                building calibration matrix. If None, the main `shots` parameter value is used.
            job_callback: Optional user supplied callback which can be used
                to monitor job progress as jobs are submitted for processing by an Aqua algorithm.
                The callback is provided the following arguments: `job_id, job_status,
                queue_position, job`
            mit_pattern: Qubits on which to perform the TensoredMeasFitter
                measurement correction, divided to groups according to tensors.
                If `None` and `qr` is given then assumed to be performed over the entire
                `qr` as one group (default `None`).
            max_job_retries(int): positive non-zero number of trials for the job set (-1 for
                infinite trials) (default: 50)

        Raises:
            QiskitError: the shots exceeds the maximum number of shots
            QiskitError: set noise model but the backend does not support that
            QiskitError: set backend_options but the backend does not support that
        """
        self._backend = backend
        self._backend_interface_version = _get_backend_interface_version(self._backend)
        self._pass_manager = pass_manager
        self._bound_pass_manager = bound_pass_manager

        # if the shots are none, try to get them from the backend
        if shots is None:
            from qiskit.providers.backend import Backend  # pylint: disable=cyclic-import

            if isinstance(backend, Backend):
                if hasattr(backend, "options"):  # should always be true for V1
                    backend_shots = backend.options.get("shots", 1024)
                    if shots != backend_shots:
                        logger.info(
                            "Overwriting the number of shots in the quantum instance with "
                            "the settings from the backend."
                        )
                    shots = backend_shots

        # safeguard if shots are still not set
        if shots is None:
            shots = 1024

        # pylint: disable=cyclic-import
        from qiskit.assembler.run_config import RunConfig

        run_config = RunConfig(shots=shots)
        if seed_simulator is not None:
            run_config.seed_simulator = seed_simulator

        self._run_config = run_config

        # setup backend config
        if self._backend_interface_version <= 1:
            basis_gates = basis_gates or backend.configuration().basis_gates
            coupling_map = coupling_map or getattr(backend.configuration(), "coupling_map", None)
            self._backend_config = {"basis_gates": basis_gates, "coupling_map": coupling_map}
        else:
            self._backend_config = {}

        # setup compile config
        self._compile_config = {
            "initial_layout": initial_layout,
            "seed_transpiler": seed_transpiler,
            "optimization_level": optimization_level,
        }

        # setup job config
        self._qjob_config = (
            {"timeout": timeout} if self.is_local else {"timeout": timeout, "wait": wait}
        )

        # setup noise config
        self._noise_config = {}
        if noise_model is not None:
            if is_simulator_backend(self._backend) and not is_basicaer_provider(self._backend):
                self._noise_config = {"noise_model": noise_model}
            else:
                raise QiskitError(
                    "The noise model is not supported "
                    "on the selected backend {} ({}) "
                    "only certain backends, such as Aer qasm simulator "
                    "support noise.".format(self.backend_name, _get_backend_provider(self._backend))
                )

        # setup backend options for run
        self._backend_options = {}
        if backend_options is not None:
            if support_backend_options(self._backend):
                self._backend_options = {"backend_options": backend_options}
            else:
                raise QiskitError(
                    "backend_options can not used with the backends in IBMQ provider."
                )

        # setup measurement error mitigation
        self._meas_error_mitigation_cls = None
        if self.is_statevector:
            if measurement_error_mitigation_cls is not None:
                raise QiskitError(
                    "Measurement error mitigation does not work with the statevector simulation."
                )
        else:
            self._meas_error_mitigation_cls = measurement_error_mitigation_cls
        self._meas_error_mitigation_fitters: Dict[str, Tuple[np.ndarray, float]] = {}
        # TODO: support different fitting method in error mitigation?
        self._meas_error_mitigation_method = "least_squares"
        self._cals_matrix_refresh_period = cals_matrix_refresh_period
        self._meas_error_mitigation_shots = measurement_error_mitigation_shots
        self._mit_pattern = mit_pattern

        if self._meas_error_mitigation_cls is not None:
            logger.info(
                "The measurement error mitigation is enabled. "
                "It will automatically submit an additional job to help "
                "calibrate the result of other jobs. "
                "The current approach will submit a job with 2^N circuits "
                "to build the calibration matrix, "
                "where N is the number of measured qubits. "
                "Furthermore, Aqua will re-use the calibration matrix for %s minutes "
                "and re-build it after that.",
                self._cals_matrix_refresh_period,
            )

        # setup others
        if is_ibmq_provider(self._backend):
            if skip_qobj_validation:
                logger.info(
                    "skip_qobj_validation was set True but this setting is not "
                    "supported by IBMQ provider and has been ignored."
                )
                skip_qobj_validation = False
        self._skip_qobj_validation = skip_qobj_validation
        self._circuit_summary = False
        self._job_callback = job_callback
        self._time_taken = 0.0
        self._max_job_retries = max_job_retries
        logger.info(self)

    def __str__(self) -> str:
        """Overload string.

        Returns:
            str: the info of the object.
        """
        from qiskit import __version__ as terra_version

        info = f"\nQiskit Terra version: {terra_version}\n"
        info += "Backend: '{} ({})', with following setting:\n{}\n{}\n{}\n{}\n{}\n{}".format(
            self.backend_name,
            _get_backend_provider(self._backend),
            self._backend_config,
            self._compile_config,
            self._run_config,
            self._qjob_config,
            self._backend_options,
            self._noise_config,
        )

        info += f"\nMeasurement mitigation: {self._meas_error_mitigation_cls}"

        return info

    @property
    def unbound_pass_manager(self):
        """Return the pass manager for designated for unbound circuits.

        Returns:
            Optional['PassManager']: The pass manager for unbound circuits, if it has been set.
        """
        return self._pass_manager

    @property
    def bound_pass_manager(self):
        """Return the pass manager for designated for bound circuits.

        Returns:
            Optional['PassManager']: The pass manager for bound circuits, if it has been set.
        """
        return self._bound_pass_manager

    def transpile(self, circuits, pass_manager=None):
        """A wrapper to transpile circuits to allow algorithm access the transpiled circuits.

        Args:
            circuits (Union['QuantumCircuit', List['QuantumCircuit']]): circuits to transpile
            pass_manager (Optional['PassManager']): A pass manager to transpile the circuits. If
                none is given, but either ``pass_manager`` or ``bound_pass_manager`` has been set
                in the initializer, these are run. If none has been provided there either, the
                backend and compile configs from the initializer are used.

        Returns:
            List['QuantumCircuit']: The transpiled circuits, it is always a list even though
                the length is one.
        """
        # pylint: disable=cyclic-import
        from qiskit import compiler
        from qiskit.transpiler import PassManager

        # if no pass manager here is given, check if they have been set in the init
        if pass_manager is None:
            # if they haven't been set in the init, use the transpile args from the init
            if self._pass_manager is None and self._bound_pass_manager is None:
                transpiled_circuits = compiler.transpile(
                    circuits, self._backend, **self._backend_config, **self._compile_config
                )
            # it they have been set, run them
            else:
                pass_manager = PassManager()
                if self._pass_manager is not None:
                    pass_manager += self._pass_manager  # check if None
                if self._bound_pass_manager is not None:
                    pass_manager += self._bound_pass_manager

                transpiled_circuits = pass_manager.run(circuits)
        # custom pass manager set by user
        else:
            transpiled_circuits = pass_manager.run(circuits)

        if not isinstance(transpiled_circuits, list):
            transpiled_circuits = [transpiled_circuits]

        if logger.isEnabledFor(logging.DEBUG) and self._circuit_summary:
            logger.debug("==== Before transpiler ====")
            logger.debug(circuit_utils.summarize_circuits(circuits))
            if transpiled_circuits is not None:
                logger.debug("====  After transpiler ====")
                logger.debug(circuit_utils.summarize_circuits(transpiled_circuits))

        return transpiled_circuits

    def assemble(self, circuits) -> Qobj:
        """assemble circuits"""
        # pylint: disable=cyclic-import
        from qiskit import compiler

        return compiler.assemble(circuits, **self._run_config.to_dict())

    def execute(self, circuits, had_transpiled: bool = False):
        """
        A wrapper to interface with quantum backend.

        Args:
            circuits (Union['QuantumCircuit', List['QuantumCircuit']]):
                        circuits to execute
            had_transpiled: whether or not circuits had been transpiled

        Raises:
            QiskitError: Invalid error mitigation fitter class
            QiskitError: TensoredMeasFitter class doesn't support subset fitter
            MissingOptionalLibraryError: Ignis not installed


        Returns:
            Result: result object

        TODO: Maybe we can combine the circuits for the main ones and calibration circuits before
              assembling to the qobj.
        """
        from qiskit.utils.run_circuits import run_circuits
        from qiskit.utils.measurement_error_mitigation import (
            get_measured_qubits,
            build_measurement_error_mitigation_circuits,
        )

        if had_transpiled:
            # Convert to a list or make a copy.
            # The measurement mitigation logic expects a list and
            # may change it in place. This makes sure that logic works
            # and any future logic that may change the input.
            # It also makes the code easier: it will always deal with a list.
            if isinstance(circuits, list):
                circuits = circuits.copy()
            else:
                circuits = [circuits]
        else:
            # transpile here, the method always returns a copied list
            circuits = self.transpile(circuits)

        if self.is_statevector and "aer_simulator_statevector" in self.backend_name:
            try:
                from qiskit.providers.aer.library import SaveStatevector

                def _find_save_state(data):
                    for instruction in reversed(data):
                        if isinstance(instruction.operation, SaveStatevector):
                            return True
                    return False

                if isinstance(circuits, list):
                    for circuit in circuits:
                        if not _find_save_state(circuit.data):
                            circuit.save_statevector()
                else:
                    if not _find_save_state(circuits.data):
                        circuits.save_statevector()
            except ImportError:
                pass

        if self._meas_error_mitigation_cls is not None:
            qubit_index, qubit_mappings = get_measured_qubits(circuits)
            mit_pattern = self._mit_pattern
            if mit_pattern is None:
                mit_pattern = [[i] for i in range(len(qubit_index))]
            qubit_index_str = "_".join([str(x) for x in qubit_index]) + "_{}".format(
                self._meas_error_mitigation_shots or self._run_config.shots
            )
            meas_error_mitigation_fitter, timestamp = self._meas_error_mitigation_fitters.get(
                qubit_index_str, (None, 0.0)
            )

            if meas_error_mitigation_fitter is None:
                # check the asked qubit_index are the subset of build matrices
                for key, _ in self._meas_error_mitigation_fitters.items():
                    stored_qubit_index = [int(x) for x in key.split("_")[:-1]]
                    stored_shots = int(key.split("_")[-1])
                    if len(qubit_index) < len(stored_qubit_index):
                        tmp = list(set(qubit_index + stored_qubit_index))
                        if (
                            sorted(tmp) == sorted(stored_qubit_index)
                            and self._run_config.shots == stored_shots
                        ):
                            # the qubit used in current job is the subset and shots are the same
                            (
                                meas_error_mitigation_fitter,
                                timestamp,
                            ) = self._meas_error_mitigation_fitters.get(key, (None, 0.0))
                            meas_error_mitigation_fitter = (
                                meas_error_mitigation_fitter.subset_fitter(
                                    qubit_sublist=qubit_index
                                )
                            )
                            logger.info(
                                "The qubits used in the current job is the subset of "
                                "previous jobs, "
                                "reusing the calibration matrix if it is not out-of-date."
                            )

            build_cals_matrix = (
                self.maybe_refresh_cals_matrix(timestamp) or meas_error_mitigation_fitter is None
            )

            cal_circuits = None
            prepended_calibration_circuits: int = 0
            if build_cals_matrix:
                logger.info("Updating to also run measurement error mitigation.")
                use_different_shots = not (
                    self._meas_error_mitigation_shots is None
                    or self._meas_error_mitigation_shots == self._run_config.shots
                )
                temp_run_config = copy.deepcopy(self._run_config)
                if use_different_shots:
                    temp_run_config.shots = self._meas_error_mitigation_shots
                (
                    cal_circuits,
                    state_labels,
                    circuit_labels,
                ) = build_measurement_error_mitigation_circuits(
                    qubit_index,
                    self._meas_error_mitigation_cls,
                    self._backend,
                    self._backend_config,
                    self._compile_config,
                    mit_pattern=mit_pattern,
                )
                if use_different_shots:
                    cals_result = run_circuits(
                        cal_circuits,
                        self._backend,
                        qjob_config=self._qjob_config,
                        backend_options=self._backend_options,
                        noise_config=self._noise_config,
                        run_config=self._run_config.to_dict(),
                        job_callback=self._job_callback,
                        max_job_retries=self._max_job_retries,
                    )
                    self._time_taken += cals_result.time_taken
                    result = run_circuits(
                        circuits,
                        self._backend,
                        qjob_config=self.qjob_config,
                        backend_options=self.backend_options,
                        noise_config=self._noise_config,
                        run_config=self.run_config.to_dict(),
                        job_callback=self._job_callback,
                        max_job_retries=self._max_job_retries,
                    )
                    self._time_taken += result.time_taken
                else:
                    circuits[0:0] = cal_circuits
                    prepended_calibration_circuits = len(cal_circuits)
                    if hasattr(self.run_config, "parameterizations"):
                        cal_run_config = copy.deepcopy(self.run_config)
                        cal_run_config.parameterizations[0:0] = [[]] * len(cal_circuits)
                    else:
                        cal_run_config = self.run_config
                    result = run_circuits(
                        circuits,
                        self._backend,
                        qjob_config=self.qjob_config,
                        backend_options=self.backend_options,
                        noise_config=self._noise_config,
                        run_config=cal_run_config.to_dict(),
                        job_callback=self._job_callback,
                        max_job_retries=self._max_job_retries,
                    )
                    self._time_taken += result.time_taken
                    cals_result = result
                logger.info("Building calibration matrix for measurement error mitigation.")
                meas_type = _MeasFitterType.type_from_class(self._meas_error_mitigation_cls)
                if meas_type == _MeasFitterType.COMPLETE_MEAS_FITTER:
                    meas_error_mitigation_fitter = self._meas_error_mitigation_cls(
                        cals_result, state_labels, qubit_list=qubit_index, circlabel=circuit_labels
                    )
                elif meas_type == _MeasFitterType.TENSORED_MEAS_FITTER:
                    meas_error_mitigation_fitter = self._meas_error_mitigation_cls(
                        cals_result, mit_pattern=state_labels, circlabel=circuit_labels
                    )
                self._meas_error_mitigation_fitters[qubit_index_str] = (
                    meas_error_mitigation_fitter,
                    time.time(),
                )
            else:
                result = run_circuits(
                    circuits,
                    self._backend,
                    qjob_config=self.qjob_config,
                    backend_options=self.backend_options,
                    noise_config=self._noise_config,
                    run_config=self._run_config.to_dict(),
                    job_callback=self._job_callback,
                    max_job_retries=self._max_job_retries,
                )
                self._time_taken += result.time_taken

            if meas_error_mitigation_fitter is not None:
                logger.info("Performing measurement error mitigation.")
                if (
                    hasattr(self._run_config, "parameterizations")
                    and len(self._run_config.parameterizations) > 0
                    and len(self._run_config.parameterizations[0]) > 0
                    and len(self._run_config.parameterizations[0][0]) > 0
                ):
                    num_circuit_templates = len(self._run_config.parameterizations)
                    num_param_variations = len(self._run_config.parameterizations[0][0])
                    num_circuits = num_circuit_templates * num_param_variations
                else:
                    input_circuits = circuits[prepended_calibration_circuits:]
                    num_circuits = len(input_circuits)
                skip_num_circuits = len(result.results) - num_circuits
                #  remove the calibration counts from result object to assure the length of
                #  ExperimentalResult is equal length to input circuits
                result.results = result.results[skip_num_circuits:]
                tmp_result = copy.deepcopy(result)
                for qubit_index_str, c_idx in qubit_mappings.items():
                    curr_qubit_index = [int(x) for x in qubit_index_str.split("_")]
                    tmp_result.results = [result.results[i] for i in c_idx]
                    if curr_qubit_index == qubit_index:
                        tmp_fitter = meas_error_mitigation_fitter
                    elif isinstance(meas_error_mitigation_fitter, TensoredMeasFitter):
                        # Different from the complete meas. fitter as only the Terra fitter
                        # implements the ``subset_fitter`` method.
                        tmp_fitter = meas_error_mitigation_fitter.subset_fitter(curr_qubit_index)
                    elif _MeasFitterType.COMPLETE_MEAS_FITTER == _MeasFitterType.type_from_instance(
                        meas_error_mitigation_fitter
                    ):
                        tmp_fitter = meas_error_mitigation_fitter.subset_fitter(curr_qubit_index)
                    else:
                        raise QiskitError(
                            "{} doesn't support subset_fitter.".format(
                                meas_error_mitigation_fitter.__class__.__name__
                            )
                        )
                    tmp_result = tmp_fitter.filter.apply(
                        tmp_result, self._meas_error_mitigation_method
                    )
                    for i, n in enumerate(c_idx):
                        # convert counts to integer and remove 0 values
                        tmp_result.results[i].data.counts = {
                            k: round(v)
                            for k, v in tmp_result.results[i].data.counts.items()
                            if round(v) != 0
                        }
                        result.results[n] = tmp_result.results[i]

        else:
            result = run_circuits(
                circuits,
                self._backend,
                qjob_config=self.qjob_config,
                backend_options=self.backend_options,
                noise_config=self._noise_config,
                run_config=self._run_config.to_dict(),
                job_callback=self._job_callback,
                max_job_retries=self._max_job_retries,
            )
            self._time_taken += result.time_taken

        if self._circuit_summary:
            self._circuit_summary = False

        return result

    def set_config(self, **kwargs):
        """Set configurations for the quantum instance."""
        for k, v in kwargs.items():
            if k in QuantumInstance._RUN_CONFIG:
                setattr(self._run_config, k, v)
            elif k in QuantumInstance._QJOB_CONFIG:
                self._qjob_config[k] = v
            elif k in QuantumInstance._COMPILE_CONFIG:
                self._compile_config[k] = v
            elif k in QuantumInstance._BACKEND_CONFIG:
                self._backend_config[k] = v
            elif k in QuantumInstance._BACKEND_OPTIONS:
                if not support_backend_options(self._backend):
                    raise QiskitError(
                        "backend_options can not be used with this backend "
                        "{} ({}).".format(self.backend_name, _get_backend_provider(self._backend))
                    )

                if k in QuantumInstance._BACKEND_OPTIONS_QASM_ONLY and self.is_statevector:
                    raise QiskitError(
                        "'{}' is only applicable for qasm simulator but "
                        "statevector simulator is used as the backend."
                    )

                if "backend_options" not in self._backend_options:
                    self._backend_options["backend_options"] = {}
                self._backend_options["backend_options"][k] = v
            elif k in QuantumInstance._NOISE_CONFIG:
                if not is_simulator_backend(self._backend) or is_basicaer_provider(self._backend):
                    raise QiskitError(
                        "The noise model is not supported on the selected backend {} ({}) "
                        "only certain backends, such as Aer qasm support "
                        "noise.".format(self.backend_name, _get_backend_provider(self._backend))
                    )

                self._noise_config[k] = v

            else:
                raise ValueError(f"unknown setting for the key ({k}).")

    @property
    def time_taken(self) -> float:
        """Accumulated time taken for execution."""
        return self._time_taken

    def reset_execution_results(self) -> None:
        """Reset execution results"""
        self._time_taken = 0.0

    @property
    def qjob_config(self):
        """Getter of qjob_config."""
        return self._qjob_config

    @property
    def backend_config(self):
        """Getter of backend_config."""
        return self._backend_config

    @property
    def compile_config(self):
        """Getter of compile_config."""
        return self._compile_config

    @property
    def run_config(self):
        """Getter of run_config."""
        return self._run_config

    @property
    def noise_config(self):
        """Getter of noise_config."""
        return self._noise_config

    @property
    def backend_options(self):
        """Getter of backend_options."""
        return self._backend_options

    @property
    def circuit_summary(self):
        """Getter of circuit summary."""
        return self._circuit_summary

    @circuit_summary.setter
    def circuit_summary(self, new_value):
        """sets circuit summary"""
        self._circuit_summary = new_value

    @property
    def max_job_retries(self):
        """Getter of max tries"""
        return self._max_job_retries

    @max_job_retries.setter
    def max_job_retries(self, new_value):
        """Sets the maximum tries"""
        if not isinstance(new_value, int):
            raise TypeError("max_job_retries parameter must be an integer")
        if new_value < -1 or new_value == 0:
            raise ValueError(
                "max_job_retries must either be a positive integer or -1(for infinite trials)"
            )
        if new_value == -1:
            self._max_job_retries = int(1e18)
        else:
            self._max_job_retries = new_value

    @property
    def measurement_error_mitigation_cls(self):
        """returns measurement error mitigation cls"""
        return self._meas_error_mitigation_cls

    @measurement_error_mitigation_cls.setter
    def measurement_error_mitigation_cls(self, new_value):
        """sets measurement error mitigation cls"""
        self._meas_error_mitigation_cls = new_value

    @property
    def cals_matrix_refresh_period(self):
        """returns matrix refresh period"""
        return self._cals_matrix_refresh_period

    @cals_matrix_refresh_period.setter
    def cals_matrix_refresh_period(self, new_value):
        """sets matrix refresh period"""
        self._cals_matrix_refresh_period = new_value

    @property
    def measurement_error_mitigation_shots(self):
        """returns measurement error mitigation shots"""
        return self._meas_error_mitigation_shots

    @measurement_error_mitigation_shots.setter
    def measurement_error_mitigation_shots(self, new_value):
        """sets measurement error mitigation shots"""
        self._meas_error_mitigation_shots = new_value

    @property
    def backend(self):
        """Return Backend backend object."""
        return self._backend

    @property
    def backend_name(self):
        """Return backend name."""
        if self._backend_interface_version <= 1:
            return self._backend.name()
        else:
            return self._backend.name

    @property
    def is_statevector(self):
        """Return True if backend is a statevector-type simulator."""
        return is_statevector_backend(self._backend)

    @property
    def is_simulator(self):
        """Return True if backend is a simulator."""
        return is_simulator_backend(self._backend)

    @property
    def is_local(self):
        """Return True if backend is a local backend."""
        return is_local_backend(self._backend)

    @property
    def skip_qobj_validation(self):
        """checks if skip qobj validation"""
        return self._skip_qobj_validation

    @skip_qobj_validation.setter
    def skip_qobj_validation(self, new_value):
        """sets skip qobj validation flag"""
        self._skip_qobj_validation = new_value

    def maybe_refresh_cals_matrix(self, timestamp: Optional[float] = None) -> bool:
        """
        Calculate the time difference from the query of last time.

        Args:
            timestamp: timestamp

        Returns:
            Whether or not refresh the cals_matrix
        """
        timestamp = timestamp or 0.0
        ret = False
        curr_timestamp = time.time()
        difference = int(curr_timestamp - timestamp) / 60.0
        if difference > self._cals_matrix_refresh_period:
            ret = True

        return ret

    def cals_matrix(
        self, qubit_index: Optional[List[int]] = None
    ) -> Optional[Union[Tuple[np.ndarray, float], Dict[str, Tuple[np.ndarray, float]]]]:
        """
        Get the stored calibration matrices and its timestamp.

        Args:
            qubit_index: the qubit index of corresponding calibration matrix.
                         If None, return all stored calibration matrices.

        Returns:
            The calibration matrix and the creation timestamp if qubit_index
            is not None otherwise, return all matrices and their timestamp
            in a dictionary.
        """
        shots = self._meas_error_mitigation_shots or self._run_config.shots
        if qubit_index:
            qubit_index_str = "_".join([str(x) for x in qubit_index]) + f"_{shots}"
            fitter, timestamp = self._meas_error_mitigation_fitters.get(qubit_index_str, None)
            if fitter is not None:
                return fitter.cal_matrix, timestamp
        else:
            return {
                k: (v.cal_matrix, t) for k, (v, t) in self._meas_error_mitigation_fitters.items()
            }
        return None