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qiskit.dagcircuit.dagdependency의 소스 코드

# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# 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.

"""DAGDependency class for representing non-commutativity in a circuit.
"""

import math
import heapq
from collections import OrderedDict, defaultdict

import rustworkx as rx

from qiskit.circuit.quantumregister import QuantumRegister, Qubit
from qiskit.circuit.classicalregister import ClassicalRegister, Clbit
from qiskit.dagcircuit.exceptions import DAGDependencyError
from qiskit.dagcircuit.dagdepnode import DAGDepNode
from qiskit.circuit.commutation_checker import CommutationChecker


# ToDo: DagDependency needs to be refactored:
#  - Removing redundant and template-optimization-specific fields from DAGDepNode:
#    As a minimum, we should remove direct predecessors and direct successors,
#    as these can be queried directly from the underlying graph data structure.
#    We should also remove fields that are specific to template-optimization pass.
#    for instance lists of transitive predecessors and successors (moreover, we
#    should investigate the possibility of using rx.descendants() instead of caching).
#  - We should rethink the API of DAGDependency:
#    Currently, most of the functions (such as "add_op_node", "_update_edges", etc.)
#    are only used when creating a new DAGDependency from another representation of a circuit.
#    On the other hand, replace_block_with_op is only used at the very end,
#    just before DAGDependency is converted into QuantumCircuit or DAGCircuit.
#    A part of the reason is that doing local changes to DAGDependency is tricky:
#    as an example, suppose that DAGDependency contains a gate A such that A = B * C;
#    in general we cannot simply replace A by the pair B, C, as there may be
#    other nodes that commute with A but do not commute with B or C, so we would need to
#    change DAGDependency more globally to support that. In other words, we should rethink
#    what DAGDependency can be good for and rethink that API accordingly.


[문서]class DAGDependency: """Object to represent a quantum circuit as a directed acyclic graph via operation dependencies (i.e. lack of commutation). The nodes in the graph are operations represented by quantum gates. The edges correspond to non-commutation between two operations (i.e. a dependency). A directed edge from node A to node B means that operation A does not commute with operation B. The object's methods allow circuits to be constructed. The nodes in the graph have the following attributes: 'operation', 'successors', 'predecessors'. **Example:** Bell circuit with no measurement. .. parsed-literal:: ┌───┐ qr_0: ┤ H ├──■── └───┘┌─┴─┐ qr_1: ─────┤ X ├ └───┘ The dependency DAG for the above circuit is represented by two nodes. The first one corresponds to Hadamard gate, the second one to the CNOT gate as the gates do not commute there is an edge between the two nodes. **Reference:** [1] Iten, R., Moyard, R., Metger, T., Sutter, D. and Woerner, S., 2020. Exact and practical pattern matching for quantum circuit optimization. `arXiv:1909.05270 <https://arxiv.org/abs/1909.05270>`_ """ def __init__(self): """ Create an empty DAGDependency. """ # Circuit name self.name = None # Circuit metadata self.metadata = None # Directed multigraph whose nodes are operations(gates) and edges # represent non-commutativity between two gates. self._multi_graph = rx.PyDAG() # Map of qreg/creg name to Register object. self.qregs = OrderedDict() self.cregs = OrderedDict() # List of all Qubit/Clbit wires. self.qubits = [] self.clbits = [] self._global_phase = 0 self._calibrations = defaultdict(dict) self.duration = None self.unit = "dt" self.comm_checker = CommutationChecker() @property def global_phase(self): """Return the global phase of the circuit.""" return self._global_phase @global_phase.setter def global_phase(self, angle): """Set the global phase of the circuit. Args: angle (float, ParameterExpression) """ from qiskit.circuit.parameterexpression import ParameterExpression # needed? if isinstance(angle, ParameterExpression): self._global_phase = angle else: # Set the phase to the [0, 2π) interval angle = float(angle) if not angle: self._global_phase = 0 else: self._global_phase = angle % (2 * math.pi) @property def calibrations(self): """Return calibration dictionary. The custom pulse definition of a given gate is of the form {'gate_name': {(qubits, params): schedule}} """ return dict(self._calibrations) @calibrations.setter def calibrations(self, calibrations): """Set the circuit calibration data from a dictionary of calibration definition. Args: calibrations (dict): A dictionary of input in the format {'gate_name': {(qubits, gate_params): schedule}} """ self._calibrations = defaultdict(dict, calibrations)
[문서] def to_retworkx(self): """Returns the DAGDependency in retworkx format.""" return self._multi_graph
[문서] def size(self): """Returns the number of gates in the circuit""" return len(self._multi_graph)
[문서] def depth(self): """Return the circuit depth. Returns: int: the circuit depth """ depth = rx.dag_longest_path_length(self._multi_graph) return depth if depth >= 0 else 0
[문서] def add_qubits(self, qubits): """Add individual qubit wires.""" if any(not isinstance(qubit, Qubit) for qubit in qubits): raise DAGDependencyError("not a Qubit instance.") duplicate_qubits = set(self.qubits).intersection(qubits) if duplicate_qubits: raise DAGDependencyError("duplicate qubits %s" % duplicate_qubits) self.qubits.extend(qubits)
[문서] def add_clbits(self, clbits): """Add individual clbit wires.""" if any(not isinstance(clbit, Clbit) for clbit in clbits): raise DAGDependencyError("not a Clbit instance.") duplicate_clbits = set(self.clbits).intersection(clbits) if duplicate_clbits: raise DAGDependencyError("duplicate clbits %s" % duplicate_clbits) self.clbits.extend(clbits)
[문서] def add_qreg(self, qreg): """Add qubits in a quantum register.""" if not isinstance(qreg, QuantumRegister): raise DAGDependencyError("not a QuantumRegister instance.") if qreg.name in self.qregs: raise DAGDependencyError("duplicate register %s" % qreg.name) self.qregs[qreg.name] = qreg existing_qubits = set(self.qubits) for j in range(qreg.size): if qreg[j] not in existing_qubits: self.qubits.append(qreg[j])
[문서] def add_creg(self, creg): """Add clbits in a classical register.""" if not isinstance(creg, ClassicalRegister): raise DAGDependencyError("not a ClassicalRegister instance.") if creg.name in self.cregs: raise DAGDependencyError("duplicate register %s" % creg.name) self.cregs[creg.name] = creg existing_clbits = set(self.clbits) for j in range(creg.size): if creg[j] not in existing_clbits: self.clbits.append(creg[j])
def _add_multi_graph_node(self, node): """ Args: node (DAGDepNode): considered node. Returns: node_id(int): corresponding label to the added node. """ node_id = self._multi_graph.add_node(node) node.node_id = node_id return node_id
[문서] def get_nodes(self): """ Returns: generator(dict): iterator over all the nodes. """ return iter(self._multi_graph.nodes())
[문서] def get_node(self, node_id): """ Args: node_id (int): label of considered node. Returns: node: corresponding to the label. """ return self._multi_graph.get_node_data(node_id)
def _add_multi_graph_edge(self, src_id, dest_id, data): """ Function to add an edge from given data (dict) between two nodes. Args: src_id (int): label of the first node. dest_id (int): label of the second node. data (dict): data contained on the edge. """ self._multi_graph.add_edge(src_id, dest_id, data)
[문서] def get_edges(self, src_id, dest_id): """ Edge enumeration between two nodes through method get_all_edge_data. Args: src_id (int): label of the first node. dest_id (int): label of the second node. Returns: List: corresponding to all edges between the two nodes. """ return self._multi_graph.get_all_edge_data(src_id, dest_id)
[문서] def get_all_edges(self): """ Enumeration of all edges. Returns: List: corresponding to the label. """ return [ (src, dest, data) for src_node in self._multi_graph.nodes() for (src, dest, data) in self._multi_graph.out_edges(src_node.node_id) ]
[문서] def get_in_edges(self, node_id): """ Enumeration of all incoming edges for a given node. Args: node_id (int): label of considered node. Returns: List: corresponding incoming edges data. """ return self._multi_graph.in_edges(node_id)
[문서] def get_out_edges(self, node_id): """ Enumeration of all outgoing edges for a given node. Args: node_id (int): label of considered node. Returns: List: corresponding outgoing edges data. """ return self._multi_graph.out_edges(node_id)
[문서] def direct_successors(self, node_id): """ Direct successors id of a given node as sorted list. Args: node_id (int): label of considered node. Returns: List: direct successors id as a sorted list """ return sorted(list(self._multi_graph.adj_direction(node_id, False).keys()))
[문서] def direct_predecessors(self, node_id): """ Direct predecessors id of a given node as sorted list. Args: node_id (int): label of considered node. Returns: List: direct predecessors id as a sorted list """ return sorted(list(self._multi_graph.adj_direction(node_id, True).keys()))
[문서] def successors(self, node_id): """ Successors id of a given node as sorted list. Args: node_id (int): label of considered node. Returns: List: all successors id as a sorted list """ return self._multi_graph.get_node_data(node_id).successors
[문서] def predecessors(self, node_id): """ Predecessors id of a given node as sorted list. Args: node_id (int): label of considered node. Returns: List: all predecessors id as a sorted list """ return self._multi_graph.get_node_data(node_id).predecessors
[문서] def topological_nodes(self): """ Yield nodes in topological order. Returns: generator(DAGNode): node in topological order. """ def _key(x): return x.sort_key return iter(rx.lexicographical_topological_sort(self._multi_graph, key=_key))
def _create_op_node(self, operation, qargs, cargs): """Creates a DAGDepNode to the graph and update the edges. Args: operation (qiskit.circuit.Operation): operation qargs (list[Qubit]): list of qubits on which the operation acts cargs (list[Clbit]): list of classical wires to attach to Returns: DAGDepNode: the newly added node. """ directives = ["measure"] if not getattr(operation, "_directive", False) and operation.name not in directives: qindices_list = [] for elem in qargs: qindices_list.append(self.qubits.index(elem)) if getattr(operation, "condition", None): # The change to handling operation.condition follows code patterns in quantum_circuit.py. # However: # (1) cindices_list are specific to template optimization and should not be computed # in this place. # (2) Template optimization pass needs currently does not handle general conditions. if isinstance(operation.condition[0], Clbit): condition_bits = [operation.condition[0]] else: condition_bits = operation.condition[0] cindices_list = [self.clbits.index(clbit) for clbit in condition_bits] else: cindices_list = [] else: qindices_list = [] cindices_list = [] new_node = DAGDepNode( type="op", op=operation, name=operation.name, qargs=qargs, cargs=cargs, successors=[], predecessors=[], qindices=qindices_list, cindices=cindices_list, ) return new_node
[문서] def add_op_node(self, operation, qargs, cargs): """Add a DAGDepNode to the graph and update the edges. Args: operation (qiskit.circuit.Operation): operation as a quantum gate qargs (list[Qubit]): list of qubits on which the operation acts cargs (list[Clbit]): list of classical wires to attach to """ new_node = self._create_op_node(operation, qargs, cargs) self._add_multi_graph_node(new_node) self._update_edges()
def _gather_pred(self, node_id, direct_pred): """Function set an attribute predecessors and gather multiple lists of direct predecessors into a single one. Args: node_id (int): label of the considered node in the DAG direct_pred (list): list of direct successors for the given node Returns: DAGDependency: A multigraph with update of the attribute ['predecessors'] the lists of direct successors are put into a single one """ gather = self._multi_graph gather.get_node_data(node_id).predecessors = [] for d_pred in direct_pred: gather.get_node_data(node_id).predecessors.append([d_pred]) pred = self._multi_graph.get_node_data(d_pred).predecessors gather.get_node_data(node_id).predecessors.append(pred) return gather def _gather_succ(self, node_id, direct_succ): """ Function set an attribute successors and gather multiple lists of direct successors into a single one. Args: node_id (int): label of the considered node in the DAG direct_succ (list): list of direct successors for the given node Returns: MultiDiGraph: with update of the attribute ['predecessors'] the lists of direct successors are put into a single one """ gather = self._multi_graph gather.get_node_data(node_id).successors = [] for d_succ in direct_succ: gather.get_node_data(node_id).successors.append([d_succ]) succ = gather.get_node_data(d_succ).successors gather.get_node_data(node_id).successors.append(succ) return gather def _list_pred(self, node_id): """ Use _gather_pred function and merge_no_duplicates to construct the list of predecessors for a given node. Args: node_id (int): label of the considered node """ direct_pred = self.direct_predecessors(node_id) self._multi_graph = self._gather_pred(node_id, direct_pred) self._multi_graph.get_node_data(node_id).predecessors = list( merge_no_duplicates(*(self._multi_graph.get_node_data(node_id).predecessors)) ) def _update_edges(self): """ Updates DagDependency by adding edges to the newly added node (max_node) from the previously added nodes. For each previously added node (prev_node), an edge from prev_node to max_node is added if max_node is "reachable" from prev_node (this means that the two nodes can be made adjacent by commuting them with other nodes), but the two nodes themselves do not commute. Currently. this function is only used when creating a new DAGDependency from another representation of a circuit, and hence there are no removed nodes (this is why iterating over all nodes is fine). """ max_node_id = len(self._multi_graph) - 1 max_node = self.get_node(max_node_id) reachable = [True] * max_node_id # Analyze nodes in the reverse topological order. # An improvement to the original algorithm is to consider only direct predecessors # and to avoid constructing the lists of forward and backward reachable predecessors # for every node when not required. for prev_node_id in range(max_node_id - 1, -1, -1): if reachable[prev_node_id]: prev_node = self.get_node(prev_node_id) if not self.comm_checker.commute( prev_node.op, prev_node.qargs, prev_node.cargs, max_node.op, max_node.qargs, max_node.cargs, ): # If prev_node and max_node do not commute, then we add an edge # between the two, and mark all direct predecessors of prev_node # as not reaching max_node. self._multi_graph.add_edge(prev_node_id, max_node_id, {"commute": False}) predecessor_ids = self._multi_graph.predecessor_indices(prev_node_id) for predecessor_id in predecessor_ids: reachable[predecessor_id] = False else: # If prev_node cannot reach max_node, then none of its predecessors can # reach max_node either. predecessor_ids = self._multi_graph.predecessor_indices(prev_node_id) for predecessor_id in predecessor_ids: reachable[predecessor_id] = False def _add_successors(self): """ Use _gather_succ and merge_no_duplicates to create the list of successors for each node. Update DAGDependency 'successors' attribute. It has to be used when the DAGDependency() object is complete (i.e. converters). """ for node_id in range(len(self._multi_graph) - 1, -1, -1): direct_successors = self.direct_successors(node_id) self._multi_graph = self._gather_succ(node_id, direct_successors) self._multi_graph.get_node_data(node_id).successors = list( merge_no_duplicates(*self._multi_graph.get_node_data(node_id).successors) ) def _add_predecessors(self): """ Use _gather_pred and merge_no_duplicates to create the list of predecessors for each node. Update DAGDependency 'predecessors' attribute. It has to be used when the DAGDependency() object is complete (i.e. converters). """ for node_id in range(0, len(self._multi_graph)): direct_predecessors = self.direct_predecessors(node_id) self._multi_graph = self._gather_pred(node_id, direct_predecessors) self._multi_graph.get_node_data(node_id).predecessors = list( merge_no_duplicates(*self._multi_graph.get_node_data(node_id).predecessors) )
[문서] def copy(self): """ Function to copy a DAGDependency object. Returns: DAGDependency: a copy of a DAGDependency object. """ dag = DAGDependency() dag.name = self.name dag.cregs = self.cregs.copy() dag.qregs = self.qregs.copy() for node in self.get_nodes(): dag._multi_graph.add_node(node.copy()) for edges in self.get_all_edges(): dag._multi_graph.add_edge(edges[0], edges[1], edges[2]) return dag
[문서] def draw(self, scale=0.7, filename=None, style="color"): """ Draws the DAGDependency graph. This function needs `pydot <https://github.com/erocarrera/pydot>`, which in turn needs Graphviz <https://www.graphviz.org/>` to be installed. Args: scale (float): scaling factor filename (str): file path to save image to (format inferred from name) style (str): 'plain': B&W graph 'color' (default): color input/output/op nodes Returns: Ipython.display.Image: if in Jupyter notebook and not saving to file, otherwise None. """ from qiskit.visualization.dag_visualization import dag_drawer return dag_drawer(dag=self, scale=scale, filename=filename, style=style)
[문서] def replace_block_with_op(self, node_block, op, wire_pos_map, cycle_check=True): """Replace a block of nodes with a single node. This is used to consolidate a block of DAGDepNodes into a single operation. A typical example is a block of CX and SWAP gates consolidated into a LinearFunction. This function is an adaptation of a similar function from DAGCircuit. It is important that such consolidation preserves commutativity assumptions present in DAGDependency. As an example, suppose that every node in a block [A, B, C, D] commutes with another node E. Let F be the consolidated node, F = A o B o C o D. Then F also commutes with E, and thus the result of replacing [A, B, C, D] by F results in a valid DAGDependency. That is, any deduction about commutativity in consolidated DAGDependency is correct. On the other hand, suppose that at least one of the nodes, say B, does not commute with E. Then the consolidated DAGDependency would imply that F does not commute with E. Even though F and E may actually commute, it is still safe to assume that they do not. That is, the current implementation of consolidation may lead to suboptimal but not to incorrect results. Args: node_block (List[DAGDepNode]): A list of dag nodes that represents the node block to be replaced op (qiskit.circuit.Operation): The operation to replace the block with wire_pos_map (Dict[Qubit, int]): The dictionary mapping the qarg to the position. This is necessary to reconstruct the qarg order over multiple gates in the combined single op node. cycle_check (bool): When set to True this method will check that replacing the provided ``node_block`` with a single node would introduce a cycle (which would invalidate the ``DAGDependency``) and will raise a ``DAGDependencyError`` if a cycle would be introduced. This checking comes with a run time penalty. If you can guarantee that your input ``node_block`` is a contiguous block and won't introduce a cycle when it's contracted to a single node, this can be set to ``False`` to improve the runtime performance of this method. Raises: DAGDependencyError: if ``cycle_check`` is set to ``True`` and replacing the specified block introduces a cycle or if ``node_block`` is empty. """ block_qargs = set() block_cargs = set() block_ids = [x.node_id for x in node_block] # If node block is empty return early if not node_block: raise DAGDependencyError("Can't replace an empty node_block") for nd in node_block: block_qargs |= set(nd.qargs) if nd.op.condition: block_cargs |= set(nd.cargs) # Create replacement node new_node = self._create_op_node( op, qargs=sorted(block_qargs, key=lambda x: wire_pos_map[x]), cargs=sorted(block_cargs, key=lambda x: wire_pos_map[x]), ) try: new_node.node_id = self._multi_graph.contract_nodes( block_ids, new_node, check_cycle=cycle_check ) except rx.DAGWouldCycle as ex: raise DAGDependencyError( "Replacing the specified node block would introduce a cycle" ) from ex
def merge_no_duplicates(*iterables): """Merge K list without duplicate using python heapq ordered merging Args: *iterables: A list of k sorted lists Yields: Iterator: List from the merging of the k ones (without duplicates """ last = object() for val in heapq.merge(*iterables): if val != last: last = val yield val