high_level_synthesis.py

# This code is part of Qiskit.
#
# (C) Copyright IBM 2022, 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.


"""

High Level Synthesis Plugins
-----------------------------

Clifford Synthesis
''''''''''''''''''

.. list-table:: Plugins for :class:`qiskit.quantum_info.Clifford` (key = ``"clifford"``)
    :header-rows: 1

    * - Plugin name
      - Plugin class
      - Targeted connectivity
      - Description
    * - ``"ag"``
      - :class:`~.AGSynthesisClifford`
      - all-to-all
      - greedily optimizes CX-count
    * - ``"bm"``
      - :class:`~.BMSynthesisClifford`
      - all-to-all
      - optimal count for `n=2,3`; used in ``"default"`` for `n=2,3`
    * - ``"greedy"``
      - :class:`~.GreedySynthesisClifford`
      - all-to-all
      - greedily optimizes CX-count; used in ``"default"`` for `n>=4`
    * - ``"layers"``
      - :class:`~.LayerSynthesisClifford`
      - all-to-all
      -
    * - ``"lnn"``
      - :class:`~.LayerLnnSynthesisClifford`
      - linear
      - many CX-gates but guarantees CX-depth of at most `7*n+2`
    * - ``"default"``
      - :class:`~.DefaultSynthesisClifford`
      - all-to-all
      - usually best for optimizing CX-count (and optimal CX-count for `n=2,3`)

.. autosummary::
   :toctree: ../stubs/

   AGSynthesisClifford
   BMSynthesisClifford
   GreedySynthesisClifford
   LayerSynthesisClifford
   LayerLnnSynthesisClifford
   DefaultSynthesisClifford


Linear Function Synthesis
'''''''''''''''''''''''''

.. list-table:: Plugins for :class:`.LinearFunction` (key = ``"linear"``)
    :header-rows: 1

    * - Plugin name
      - Plugin class
      - Targeted connectivity
      - Description
    * - ``"kms"``
      - :class:`~.KMSSynthesisLinearFunction`
      - linear
      - many CX-gates but guarantees CX-depth of at most `5*n`
    * - ``"pmh"``
      - :class:`~.PMHSynthesisLinearFunction`
      - all-to-all
      - greedily optimizes CX-count; used in ``"default"``
    * - ``"default"``
      - :class:`~.DefaultSynthesisLinearFunction`
      - all-to-all
      - best for optimizing CX-count

.. autosummary::
   :toctree: ../stubs/

   KMSSynthesisLinearFunction
   PMHSynthesisLinearFunction
   DefaultSynthesisLinearFunction


Permutation Synthesis
'''''''''''''''''''''

.. list-table:: Plugins for :class:`.PermutationGate` (key = ``"permutation"``)
    :header-rows: 1

    * - Plugin name
      - Plugin class
      - Targeted connectivity
      - Description
    * - ``"basic"``
      - :class:`~.BasicSynthesisPermutation`
      - all-to-all
      - optimal SWAP-count; used in ``"default"``
    * - ``"acg"``
      - :class:`~.ACGSynthesisPermutation`
      - all-to-all
      - guarantees SWAP-depth of at most `2`
    * - ``"kms"``
      - :class:`~.KMSSynthesisPermutation`
      - linear
      - many SWAP-gates, but guarantees SWAP-depth of at most `n`
    * - ``"token_swapper"``
      - :class:`~.TokenSwapperSynthesisPermutation`
      - any
      - greedily optimizes SWAP-count for arbitrary connectivity
    * - ``"default"``
      - :class:`~.BasicSynthesisPermutation`
      - all-to-all
      - best for optimizing SWAP-count

.. autosummary::
   :toctree: ../stubs/

   BasicSynthesisPermutation
   ACGSynthesisPermutation
   KMSSynthesisPermutation
   TokenSwapperSynthesisPermutation
"""

from typing import Optional, Union, List, Tuple

import numpy as np
import rustworkx as rx

from qiskit.circuit.operation import Operation
from qiskit.converters import circuit_to_dag, dag_to_circuit
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.circuit.quantumcircuit import QuantumCircuit
from qiskit.circuit import ControlFlowOp, ControlledGate, EquivalenceLibrary
from qiskit.circuit.library import LinearFunction
from qiskit.transpiler.passes.utils import control_flow
from qiskit.transpiler.target import Target
from qiskit.transpiler.coupling import CouplingMap
from qiskit.dagcircuit.dagcircuit import DAGCircuit
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.transpiler.passes.routing.algorithms import ApproximateTokenSwapper

from qiskit.circuit.annotated_operation import (
    AnnotatedOperation,
    InverseModifier,
    ControlModifier,
    PowerModifier,
)
from qiskit.synthesis.clifford import (
    synth_clifford_full,
    synth_clifford_layers,
    synth_clifford_depth_lnn,
    synth_clifford_greedy,
    synth_clifford_ag,
    synth_clifford_bm,
)
from qiskit.synthesis.linear import (
    synth_cnot_count_full_pmh,
    synth_cnot_depth_line_kms,
    calc_inverse_matrix,
)
from qiskit.synthesis.linear.linear_circuits_utils import transpose_cx_circ
from qiskit.synthesis.permutation import (
    synth_permutation_basic,
    synth_permutation_acg,
    synth_permutation_depth_lnn_kms,
)

from .plugin import HighLevelSynthesisPluginManager, HighLevelSynthesisPlugin


class HLSConfig:
    """The high-level-synthesis config allows to specify a list of "methods" used by
    :class:`~.HighLevelSynthesis` transformation pass to synthesize different types
    of higher-level objects.

    A higher-level object is an object of type :class:`~.Operation` (e.g., :class:`.Clifford` or
    :class:`.LinearFunction`).  Each object is referred to by its :attr:`~.Operation.name` field
    (e.g., ``"clifford"`` for :class:`.Clifford` objects), and the applicable synthesis methods are
    tied to this name.

    In the config, each method is specified in one of several ways:

    1. a tuple consisting of the name of a known synthesis plugin and a dictionary providing
       additional arguments for the algorithm.
    2. a tuple consisting of an instance of :class:`.HighLevelSynthesisPlugin` and additional
       arguments for the algorithm.
    3. a single string of a known synthesis plugin
    4. a single instance of :class:`.HighLevelSynthesisPlugin`.

    The following example illustrates different ways how a config file can be created::

        from qiskit.transpiler.passes.synthesis.high_level_synthesis import HLSConfig
        from qiskit.transpiler.passes.synthesis.high_level_synthesis import ACGSynthesisPermutation

        # All the ways to specify hls_config are equivalent
        hls_config = HLSConfig(permutation=[("acg", {})])
        hls_config = HLSConfig(permutation=["acg"])
        hls_config = HLSConfig(permutation=[(ACGSynthesisPermutation(), {})])
        hls_config = HLSConfig(permutation=[ACGSynthesisPermutation()])

    The names of the synthesis plugins should be declared in ``entry-points`` table for
    ``qiskit.synthesis`` in ``pyproject.toml``, in the form
    <higher-level-object-name>.<synthesis-method-name>.

    The standard higher-level-objects are recommended to have a synthesis method
    called "default", which would be called automatically when synthesizing these objects,
    without having to explicitly set these methods in the config.

    To avoid synthesizing a given higher-level-object, one can give it an empty list of methods.

    For an explicit example of using such config files, refer to the documentation for
    :class:`~.HighLevelSynthesis`.

    For an overview of the complete process of using high-level synthesis, see
    :ref:`using-high-level-synthesis-plugins`.
    """

    def __init__(self, use_default_on_unspecified=True, **kwargs):
        """Creates a high-level-synthesis config.

        Args:
            use_default_on_unspecified (bool): if True, every higher-level-object without an
                explicitly specified list of methods will be synthesized using the "default"
                algorithm if it exists.
            kwargs: a dictionary mapping higher-level-objects to lists of synthesis methods.
        """
        self.use_default_on_unspecified = use_default_on_unspecified
        self.methods = {}

        for key, value in kwargs.items():
            self.set_methods(key, value)

    def set_methods(self, hls_name, hls_methods):
        """Sets the list of synthesis methods for a given higher-level-object. This overwrites
        the lists of methods if also set previously."""
        self.methods[hls_name] = hls_methods


# ToDo: Do we have a way to specify optimization criteria (e.g., 2q gate count vs. depth)?


class HighLevelSynthesis(TransformationPass):
    """Synthesize higher-level objects and unroll custom definitions.

    The input to this pass is a DAG that may contain higher-level objects,
    including abstract mathematical objects (e.g., objects of type :class:`.LinearFunction`),
    annotated operations (objects of type :class:`.AnnotatedOperation`), and
    custom gates.

    In the most common use-case when either ``basis_gates`` or ``target`` is specified,
    all higher-level objects are synthesized, so the output is a :class:`.DAGCircuit`
    without such objects.
    More precisely, every gate in the output DAG is either directly supported by the target,
    or is in ``equivalence_library``.

    The abstract mathematical objects are synthesized using synthesis plugins, applying
    synthesis methods specified in the high-level-synthesis config (refer to the documentation
    for :class:`~.HLSConfig`).

    As an example, let us assume that ``op_a`` and ``op_b`` are names of two higher-level objects,
    that ``op_a``-objects have two synthesis methods ``default`` which does require any additional
    parameters and ``other`` with two optional integer parameters ``option_1`` and ``option_2``,
    that ``op_b``-objects have a single synthesis method ``default``, and ``qc`` is a quantum
    circuit containing ``op_a`` and ``op_b`` objects. The following code snippet::

        hls_config = HLSConfig(op_b=[("other", {"option_1": 7, "option_2": 4})])
        pm = PassManager([HighLevelSynthesis(hls_config=hls_config)])
        transpiled_qc = pm.run(qc)

    shows how to run the alternative synthesis method ``other`` for ``op_b``-objects, while using the
    ``default`` methods for all other high-level objects, including ``op_a``-objects.

    The annotated operations (consisting of a base operation and a list of inverse, control and power
    modifiers) are synthesizing recursively, first synthesizing the base operation, and then applying
    synthesis methods for creating inverted, controlled, or powered versions of that).

    The custom gates are synthesized by recursively unrolling their definitions, until every gate
    is either supported by the target or is in the equivalence library.

    When neither ``basis_gates`` nor ``target`` is specified, the pass synthesizes only the top-level
    abstract mathematical objects and annotated operations, without descending into the gate
    ``definitions``. This is consistent with the older behavior of the pass, allowing to synthesize
    some higher-level objects using plugins and leaving the other gates untouched.
    """

    def __init__(
        self,
        hls_config: Optional[HLSConfig] = None,
        coupling_map: Optional[CouplingMap] = None,
        target: Optional[Target] = None,
        use_qubit_indices: bool = False,
        equivalence_library: Optional[EquivalenceLibrary] = None,
        basis_gates: Optional[List[str]] = None,
        min_qubits: int = 0,
    ):
        """
        HighLevelSynthesis initializer.

        Args:
            hls_config: Optional, the high-level-synthesis config that specifies synthesis methods
                and parameters for various high-level-objects in the circuit. If it is not specified,
                the default synthesis methods and parameters will be used.
            coupling_map: Optional, directed graph represented as a coupling map.
            target: Optional, the backend target to use for this pass. If it is specified,
                it will be used instead of the coupling map.
            use_qubit_indices: a flag indicating whether this synthesis pass is running before or after
                the layout is set, that is, whether the qubit indices of higher-level-objects correspond
                to qubit indices on the target backend.
            equivalence_library: The equivalence library used (instructions in this library will not
                be unrolled by this pass).
            basis_gates: Optional, target basis names to unroll to, e.g. `['u3', 'cx']`.
                Ignored if ``target`` is also specified.
            min_qubits: The minimum number of qubits for operations in the input
                dag to translate.
        """
        super().__init__()

        if hls_config is not None:
            self.hls_config = hls_config
        else:
            # When the config file is not provided, we will use the "default" method
            # to synthesize Operations (when available).
            self.hls_config = HLSConfig(True)

        self.hls_plugin_manager = HighLevelSynthesisPluginManager()
        self._coupling_map = coupling_map
        self._target = target
        self._use_qubit_indices = use_qubit_indices
        if target is not None:
            self._coupling_map = self._target.build_coupling_map()
        self._equiv_lib = equivalence_library
        self._basis_gates = basis_gates
        self._min_qubits = min_qubits

        self._top_level_only = self._basis_gates is None and self._target is None

        # include path for when target exists but target.num_qubits is None (BasicSimulator)
        if not self._top_level_only and (self._target is None or self._target.num_qubits is None):
            basic_insts = {"measure", "reset", "barrier", "snapshot", "delay"}
            self._device_insts = basic_insts | set(self._basis_gates)

    def run(self, dag: DAGCircuit) -> DAGCircuit:
        """Run the HighLevelSynthesis pass on `dag`.

        Args:
            dag: input dag.

        Returns:
            Output dag with higher-level operations synthesized.

        Raises:
            TranspilerError: when the transpiler is unable to synthesize the given DAG
            (for instance, when the specified synthesis method is not available).
        """

        # copy dag_op_nodes because we are modifying the DAG below
        dag_op_nodes = dag.op_nodes()

        for node in dag_op_nodes:
            if isinstance(node.op, ControlFlowOp):
                node.op = control_flow.map_blocks(self.run, node.op)
                continue

            if getattr(node.op, "_directive", False):
                continue

            if dag.has_calibration_for(node) or len(node.qargs) < self._min_qubits:
                continue

            qubits = (
                [dag.find_bit(x).index for x in node.qargs] if self._use_qubit_indices else None
            )

            decomposition, modified = self._recursively_handle_op(node.op, qubits)

            if not modified:
                continue

            if isinstance(decomposition, QuantumCircuit):
                dag.substitute_node_with_dag(
                    node, circuit_to_dag(decomposition, copy_operations=False)
                )
            elif isinstance(decomposition, DAGCircuit):
                dag.substitute_node_with_dag(node, decomposition)
            elif isinstance(decomposition, Operation):
                dag.substitute_node(node, decomposition)

        return dag

    def _recursively_handle_op(
        self, op: Operation, qubits: Optional[List] = None
    ) -> Tuple[Union[QuantumCircuit, DAGCircuit, Operation], bool]:
        """Recursively synthesizes a single operation.

        Note: the reason that this function accepts an operation and not a dag node
        is that it's also used for synthesizing the base operation for an annotated
        gate (i.e. no dag node is available).

        There are several possible results:

        - The given operation is unchanged: e.g., it is supported by the target or is
          in the equivalence library
        - The result is a quantum circuit: e.g., synthesizing Clifford using plugin
        - The result is a DAGCircuit: e.g., when unrolling custom gates
        - The result is an Operation: e.g., adding control to CXGate results in CCXGate
        - The given operation could not be synthesized, raising a transpiler error

        The function returns the result of the synthesis (either a quantum circuit or
        an Operation), and, as an optimization, a boolean indicating whether
        synthesis did anything.

        The function is recursive, for example synthesizing an annotated operation
        involves synthesizing its "base operation" which might also be
        an annotated operation.
        """

        # Try to apply plugin mechanism
        decomposition = self._synthesize_op_using_plugins(op, qubits)
        if decomposition is not None:
            return decomposition, True

        # Handle annotated operations
        decomposition = self._synthesize_annotated_op(op)
        if decomposition:
            return decomposition, True

        # Don't do anything else if processing only top-level
        if self._top_level_only:
            return op, False

        # For non-controlled-gates, check if it's already supported by the target
        # or is in equivalence library
        controlled_gate_open_ctrl = isinstance(op, ControlledGate) and op._open_ctrl
        if not controlled_gate_open_ctrl:
            qargs = tuple(qubits) if qubits is not None else None
            # include path for when target exists but target.num_qubits is None (BasicSimulator)
            inst_supported = (
                self._target.instruction_supported(
                    operation_name=op.name,
                    qargs=qargs,
                )
                if self._target is not None and self._target.num_qubits is not None
                else op.name in self._device_insts
            )
            if inst_supported or (self._equiv_lib is not None and self._equiv_lib.has_entry(op)):
                return op, False

        try:
            # extract definition
            definition = op.definition
        except TypeError as err:
            raise TranspilerError(
                f"HighLevelSynthesis was unable to extract definition for {op.name}: {err}"
            ) from err
        except AttributeError:
            # definition is None
            definition = None

        if definition is None:
            raise TranspilerError(f"HighLevelSynthesis was unable to synthesize {op}.")

        dag = circuit_to_dag(definition, copy_operations=False)
        dag = self.run(dag)
        return dag, True

    def _synthesize_op_using_plugins(
        self, op: Operation, qubits: List
    ) -> Union[QuantumCircuit, None]:
        """
        Attempts to synthesize op using plugin mechanism.
        Returns either the synthesized circuit or None (which occurs when no
        synthesis methods are available or specified).
        """
        hls_plugin_manager = self.hls_plugin_manager

        if op.name in self.hls_config.methods.keys():
            # the operation's name appears in the user-provided config,
            # we use the list of methods provided by the user
            methods = self.hls_config.methods[op.name]
        elif (
            self.hls_config.use_default_on_unspecified
            and "default" in hls_plugin_manager.method_names(op.name)
        ):
            # the operation's name does not appear in the user-specified config,
            # we use the "default" method when instructed to do so and the "default"
            # method is available
            methods = ["default"]
        else:
            methods = []

        for method in methods:
            # There are two ways to specify a synthesis method. The more explicit
            # way is to specify it as a tuple consisting of a synthesis algorithm and a
            # list of additional arguments, e.g.,
            #   ("kms", {"all_mats": 1, "max_paths": 100, "orig_circuit": 0}), or
            #   ("pmh", {}).
            # When the list of additional arguments is empty, one can also specify
            # just the synthesis algorithm, e.g.,
            #   "pmh".
            if isinstance(method, tuple):
                plugin_specifier, plugin_args = method
            else:
                plugin_specifier = method
                plugin_args = {}

            # There are two ways to specify a synthesis algorithm being run,
            # either by name, e.g. "kms" (which then should be specified in entry_points),
            # or directly as a class inherited from HighLevelSynthesisPlugin (which then
            # does not need to be specified in entry_points).
            if isinstance(plugin_specifier, str):
                if plugin_specifier not in hls_plugin_manager.method_names(op.name):
                    raise TranspilerError(
                        "Specified method: %s not found in available plugins for %s"
                        % (plugin_specifier, op.name)
                    )
                plugin_method = hls_plugin_manager.method(op.name, plugin_specifier)
            else:
                plugin_method = plugin_specifier

            decomposition = plugin_method.run(
                op,
                coupling_map=self._coupling_map,
                target=self._target,
                qubits=qubits,
                **plugin_args,
            )

            # The synthesis methods that are not suited for the given higher-level-object
            # will return None, in which case the next method in the list will be used.
            if decomposition is not None:
                return decomposition

        return None

    def _synthesize_annotated_op(self, op: Operation) -> Union[Operation, None]:
        """
        Recursively synthesizes annotated operations.
        Returns either the synthesized operation or None (which occurs when the operation
        is not an annotated operation).
        """
        if isinstance(op, AnnotatedOperation):
            # Recursively handle the base operation
            # This results in QuantumCircuit, DAGCircuit or Gate
            synthesized_op, _ = self._recursively_handle_op(op.base_op, qubits=None)

            if isinstance(synthesized_op, AnnotatedOperation):
                raise TranspilerError(
                    "HighLevelSynthesis failed to synthesize the base operation of"
                    " an annotated operation."
                )

            for modifier in op.modifiers:
                # If we have a DAGCircuit at this point, convert it to QuantumCircuit
                if isinstance(synthesized_op, DAGCircuit):
                    synthesized_op = dag_to_circuit(synthesized_op, copy_operations=False)

                if isinstance(modifier, InverseModifier):
                    # Both QuantumCircuit and Gate have inverse method
                    synthesized_op = synthesized_op.inverse()

                elif isinstance(modifier, ControlModifier):
                    # Both QuantumCircuit and Gate have control method, however for circuits
                    # it is more efficient to avoid constructing the controlled quantum circuit.
                    if isinstance(synthesized_op, QuantumCircuit):
                        synthesized_op = synthesized_op.to_gate()

                    synthesized_op = synthesized_op.control(
                        num_ctrl_qubits=modifier.num_ctrl_qubits,
                        label=None,
                        ctrl_state=modifier.ctrl_state,
                        annotated=False,
                    )

                    if isinstance(synthesized_op, AnnotatedOperation):
                        raise TranspilerError(
                            "HighLevelSynthesis failed to synthesize the control modifier."
                        )

                    # Unrolling
                    synthesized_op, _ = self._recursively_handle_op(synthesized_op)

                elif isinstance(modifier, PowerModifier):
                    # QuantumCircuit has power method, and Gate needs to be converted
                    # to a quantum circuit.
                    if isinstance(synthesized_op, QuantumCircuit):
                        qc = synthesized_op
                    else:
                        qc = QuantumCircuit(synthesized_op.num_qubits, synthesized_op.num_clbits)
                        qc.append(
                            synthesized_op,
                            range(synthesized_op.num_qubits),
                            range(synthesized_op.num_clbits),
                        )

                    qc = qc.power(modifier.power)
                    synthesized_op = qc.to_gate()

                    # Unrolling
                    synthesized_op, _ = self._recursively_handle_op(synthesized_op)

                else:
                    raise TranspilerError(f"Unknown modifier {modifier}.")

            return synthesized_op
        return None


class DefaultSynthesisClifford(HighLevelSynthesisPlugin):
    """The default clifford synthesis plugin.

    For N <= 3 qubits this is the optimal CX cost decomposition by Bravyi, Maslov.
    For N > 3 qubits this is done using the general non-optimal greedy compilation
    routine from reference by Bravyi, Hu, Maslov, Shaydulin.

    This plugin name is :``clifford.default`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        decomposition = synth_clifford_full(high_level_object)
        return decomposition


class AGSynthesisClifford(HighLevelSynthesisPlugin):
    """Clifford synthesis plugin based on the Aaronson-Gottesman method.

    This plugin name is :``clifford.ag`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        decomposition = synth_clifford_ag(high_level_object)
        return decomposition


class BMSynthesisClifford(HighLevelSynthesisPlugin):
    """Clifford synthesis plugin based on the Bravyi-Maslov method.

    The method only works on Cliffords with at most 3 qubits, for which it
    constructs the optimal CX cost decomposition.

    This plugin name is :``clifford.bm`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        if high_level_object.num_qubits <= 3:
            decomposition = synth_clifford_bm(high_level_object)
        else:
            decomposition = None
        return decomposition


class GreedySynthesisClifford(HighLevelSynthesisPlugin):
    """Clifford synthesis plugin based on the greedy synthesis
    Bravyi-Hu-Maslov-Shaydulin method.

    This plugin name is :``clifford.greedy`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        decomposition = synth_clifford_greedy(high_level_object)
        return decomposition


class LayerSynthesisClifford(HighLevelSynthesisPlugin):
    """Clifford synthesis plugin based on the Bravyi-Maslov method
    to synthesize Cliffords into layers.

    This plugin name is :``clifford.layers`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        decomposition = synth_clifford_layers(high_level_object)
        return decomposition


class LayerLnnSynthesisClifford(HighLevelSynthesisPlugin):
    """Clifford synthesis plugin based on the Bravyi-Maslov method
    to synthesize Cliffords into layers, with each layer synthesized
    adhering to LNN connectivity.

    This plugin name is :``clifford.lnn`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Clifford."""
        decomposition = synth_clifford_depth_lnn(high_level_object)
        return decomposition


class DefaultSynthesisLinearFunction(HighLevelSynthesisPlugin):
    """The default linear function synthesis plugin.

    This plugin name is :``linear_function.default`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given LinearFunction."""
        decomposition = synth_cnot_count_full_pmh(high_level_object.linear)
        return decomposition


class KMSSynthesisLinearFunction(HighLevelSynthesisPlugin):
    """Linear function synthesis plugin based on the Kutin-Moulton-Smithline method.

    This plugin name is :``linear_function.kms`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.

    The plugin supports the following plugin-specific options:

    * use_inverted: Indicates whether to run the algorithm on the inverse matrix
        and to invert the synthesized circuit.
        In certain cases this provides a better decomposition than the direct approach.
    * use_transposed: Indicates whether to run the algorithm on the transposed matrix
        and to invert the order of CX gates in the synthesized circuit.
        In certain cases this provides a better decomposition than the direct approach.

    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given LinearFunction."""

        if not isinstance(high_level_object, LinearFunction):
            raise TranspilerError(
                "PMHSynthesisLinearFunction only accepts objects of type LinearFunction"
            )

        use_inverted = options.get("use_inverted", False)
        use_transposed = options.get("use_transposed", False)

        mat = high_level_object.linear.astype(int)

        if use_transposed:
            mat = np.transpose(mat)
        if use_inverted:
            mat = calc_inverse_matrix(mat)

        decomposition = synth_cnot_depth_line_kms(mat)

        if use_transposed:
            decomposition = transpose_cx_circ(decomposition)
        if use_inverted:
            decomposition = decomposition.inverse()

        return decomposition


class PMHSynthesisLinearFunction(HighLevelSynthesisPlugin):
    """Linear function synthesis plugin based on the Patel-Markov-Hayes method.

    This plugin name is :``linear_function.pmh`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.

    The plugin supports the following plugin-specific options:

    * section size: The size of each section used in the Patel–Markov–Hayes algorithm [1].
    * use_inverted: Indicates whether to run the algorithm on the inverse matrix
        and to invert the synthesized circuit.
        In certain cases this provides a better decomposition than the direct approach.
    * use_transposed: Indicates whether to run the algorithm on the transposed matrix
        and to invert the order of CX gates in the synthesized circuit.
        In certain cases this provides a better decomposition than the direct approach.

    References:
        1. Patel, Ketan N., Igor L. Markov, and John P. Hayes,
           *Optimal synthesis of linear reversible circuits*,
           Quantum Information & Computation 8.3 (2008): 282-294.
           `arXiv:quant-ph/0302002 [quant-ph] <https://arxiv.org/abs/quant-ph/0302002>`_
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given LinearFunction."""

        if not isinstance(high_level_object, LinearFunction):
            raise TranspilerError(
                "PMHSynthesisLinearFunction only accepts objects of type LinearFunction"
            )

        section_size = options.get("section_size", 2)
        use_inverted = options.get("use_inverted", False)
        use_transposed = options.get("use_transposed", False)

        mat = high_level_object.linear.astype(int)

        if use_transposed:
            mat = np.transpose(mat)
        if use_inverted:
            mat = calc_inverse_matrix(mat)

        decomposition = synth_cnot_count_full_pmh(mat, section_size=section_size)

        if use_transposed:
            decomposition = transpose_cx_circ(decomposition)
        if use_inverted:
            decomposition = decomposition.inverse()

        return decomposition


class KMSSynthesisPermutation(HighLevelSynthesisPlugin):
    """The permutation synthesis plugin based on the Kutin, Moulton, Smithline method.

    This plugin name is :``permutation.kms`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Permutation."""
        decomposition = synth_permutation_depth_lnn_kms(high_level_object.pattern)
        return decomposition


class BasicSynthesisPermutation(HighLevelSynthesisPlugin):
    """The permutation synthesis plugin based on sorting.

    This plugin name is :``permutation.basic`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Permutation."""
        decomposition = synth_permutation_basic(high_level_object.pattern)
        return decomposition


class ACGSynthesisPermutation(HighLevelSynthesisPlugin):
    """The permutation synthesis plugin based on the Alon, Chung, Graham method.

    This plugin name is :``permutation.acg`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.
    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Permutation."""
        decomposition = synth_permutation_acg(high_level_object.pattern)
        return decomposition


class TokenSwapperSynthesisPermutation(HighLevelSynthesisPlugin):
    """The permutation synthesis plugin based on the token swapper algorithm.

    This plugin name is :``permutation.token_swapper`` which can be used as the key on
    an :class:`~.HLSConfig` object to use this method with :class:`~.HighLevelSynthesis`.

    In more detail, this plugin is used to synthesize objects of type `PermutationGate`.
    When synthesis succeeds, the plugin outputs a quantum circuit consisting only of swap
    gates. When synthesis does not succeed, the plugin outputs `None`.

    If either `coupling_map` or `qubits` is None, then the synthesized circuit
    is not required to adhere to connectivity constraints, as is the case
    when the synthesis is done before layout/routing.

    On the other hand, if both `coupling_map` and `qubits` are specified, the synthesized
    circuit is supposed to adhere to connectivity constraints. At the moment, the
    plugin only creates swap gates between qubits in `qubits`, i.e. it does not use
    any other qubits in the coupling map (if such synthesis is not possible, the
    plugin  outputs `None`).

    The plugin supports the following plugin-specific options:

    * trials: The number of trials for the token swapper to perform the mapping. The
      circuit with the smallest number of SWAPs is returned.
    * seed: The argument to the token swapper specifying the seed for random trials.
    * parallel_threshold: The argument to the token swapper specifying the number of nodes
      in the graph beyond which the algorithm will use parallel processing.

    For more details on the token swapper algorithm, see to the paper:
    `arXiv:1902.09102 <https://arxiv.org/abs/1902.09102>`__.

    """

    def run(self, high_level_object, coupling_map=None, target=None, qubits=None, **options):
        """Run synthesis for the given Permutation."""

        trials = options.get("trials", 5)
        seed = options.get("seed", 0)
        parallel_threshold = options.get("parallel_threshold", 50)

        pattern = high_level_object.pattern
        pattern_as_dict = {j: i for i, j in enumerate(pattern)}

        # When the plugin is called from the HighLevelSynthesis transpiler pass,
        # the coupling map already takes target into account.
        if coupling_map is None or qubits is None:
            # The abstract synthesis uses a fully connected coupling map, allowing
            # arbitrary connections between qubits.
            used_coupling_map = CouplingMap.from_full(len(pattern))
        else:
            # The concrete synthesis uses the coupling map restricted to the set of
            # qubits over which the permutation gate is defined. If we allow using other
            # qubits in the coupling map, replacing the node in the DAGCircuit that
            # defines this PermutationGate by the DAG corresponding to the constructed
            # decomposition becomes problematic. Note that we allow the reduced
            # coupling map to be disconnected.
            used_coupling_map = coupling_map.reduce(qubits, check_if_connected=False)

        graph = used_coupling_map.graph.to_undirected()
        swapper = ApproximateTokenSwapper(graph, seed=seed)

        try:
            swapper_result = swapper.map(
                pattern_as_dict, trials, parallel_threshold=parallel_threshold
            )
        except rx.InvalidMapping:
            swapper_result = None

        if swapper_result is not None:
            decomposition = QuantumCircuit(len(graph.node_indices()))
            for swap in swapper_result:
                decomposition.swap(*swap)
            return decomposition

        return None