Source code for pennylane.transforms.commutation_dag

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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"""
A transform to obtain the commutation DAG of a quantum circuit.
"""
import heapq
from functools import wraps
from collections import OrderedDict
from networkx.drawing.nx_pydot import to_pydot

import networkx as nx
import pennylane as qml
from pennylane.wires import Wires


[docs]def commutation_dag(circuit): r"""Construct the pairwise-commutation DAG (directed acyclic graph) representation of a quantum circuit. In the DAG, each node represents a quantum operation, and edges represent non-commutation between two operations. This transform takes into account that not all operations can be moved next to each other by pairwise commutation. Args: circuit (pennylane.QNode, .QuantumTape, or Callable): A quantum node, tape, or function that applies quantum operations. Returns: function: Function which accepts the same arguments as the :class:`qml.QNode`, :class:`qml.tape.QuantumTape` or quantum function. When called, this function will return the commutation DAG representation of the circuit. **Example** .. code-block:: python def circuit(x, y, z): qml.RX(x, wires=0) qml.RX(y, wires=0) qml.CNOT(wires=[1, 2]) qml.RY(y, wires=1) qml.Hadamard(wires=2) qml.CRZ(z, wires=[2, 0]) qml.RY(-y, wires=1) return qml.expval(qml.PauliZ(0)) The commutation dag can be returned by using the following code: >>> dag_fn = commutation_dag(circuit) >>> dag = dag_fn(np.pi / 4, np.pi / 3, np.pi / 2) Nodes in the commutation DAG can be accessed via the :meth:`~.get_nodes` method, returning a list of the form ``(ID, CommutationDAGNode)``: >>> nodes = dag.get_nodes() >>> nodes NodeDataView({0: <pennylane.transforms.commutation_dag.CommutationDAGNode object at 0x7f461c4bb580>, ...}, data='node') You can also access specific nodes (of type :class:`~.CommutationDAGNode`) by using the :meth:`~.get_node` method. See :class:`~.CommutationDAGNode` for a list of available node attributes. >>> second_node = dag.get_node(2) >>> second_node <pennylane.transforms.commutation_dag.CommutationDAGNode object at 0x136f8c4c0> >>> second_node.op CNOT(wires=[1, 2]) >>> second_node.successors [3, 4, 5, 6] >>> second_node.predecessors [] For more details, see: * Iten, R., Moyard, R., Metger, T., Sutter, D., Woerner, S. "Exact and practical pattern matching for quantum circuit optimization" `doi.org/10.1145/3498325 <https://dl.acm.org/doi/abs/10.1145/3498325>`_ """ # pylint: disable=protected-access @wraps(circuit) def wrapper(*args, **kwargs): if isinstance(circuit, qml.QNode): # user passed a QNode, get the tape circuit.construct(args, kwargs) tape = circuit.qtape elif isinstance(circuit, qml.tape.QuantumTape): # user passed a tape tape = circuit elif callable(circuit): # user passed something that is callable but not a tape or qnode. tape = qml.transforms.make_tape(circuit)(*args, **kwargs) # raise exception if it is not a quantum function if len(tape.operations) == 0: raise ValueError("Function contains no quantum operation") else: raise ValueError("Input is not a tape, QNode, or quantum function") # Initialize DAG dag = CommutationDAG(tape) return dag return wrapper
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
[docs]class CommutationDAGNode: r"""Class to store information about a quantum operation in a node of the commutation DAG. Args: op (.Operation): PennyLane operation. wires (.Wires): Wires on which the operation acts on. node_id (int): ID of the node in the DAG. successors (array[int]): List of the node's successors in the DAG. predecessors (array[int]): List of the node's predecessors in the DAG. reachable (bool): Attribute used to check reachability by pairwise commutation. """ # pylint: disable=too-many-instance-attributes # pylint: disable=too-many-arguments # pylint: disable=too-few-public-methods __slots__ = [ "op", "wires", "target_wires", "control_wires", "node_id", "successors", "predecessors", "reachable", ] def __init__( self, op=None, wires=None, target_wires=None, control_wires=None, successors=None, predecessors=None, reachable=None, node_id=-1, ): self.op = op """Operation: The operation represented by the nodes.""" self.wires = wires """Wires: The wires that the operation acts on.""" self.target_wires = target_wires """Wires: The target wires of the operation.""" self.control_wires = control_wires if control_wires is not None else [] """Wires: The control wires of the operation.""" self.node_id = node_id """int: The ID of the operation in the DAG.""" self.successors = successors if successors is not None else [] """list(int): List of the node's successors.""" self.predecessors = predecessors if predecessors is not None else [] """list(int): List of the node's predecessors.""" self.reachable = reachable """bool: Useful attribute to create the commutation DAG."""
[docs]class CommutationDAG: r"""Class to represent a quantum circuit as a directed acyclic graph (DAG). This class is useful to build the commutation DAG and set up all nodes attributes. The construction of the DAG should be used through the transform :class:`qml.transforms.commutation_dag`. Args: tape (.QuantumTape): PennyLane quantum tape representing a quantum circuit. **Reference:** [1] Iten, R., Moyard, R., Metger, T., Sutter, D. and Woerner, S., 2020. Exact and practical pattern matching for quantum circuit optimization. `doi.org/10.1145/3498325 <https://dl.acm.org/doi/abs/10.1145/3498325>`_ """ def __init__(self, tape): self.num_wires = len(tape.wires) self.node_id = -1 self._multi_graph = nx.MultiDiGraph() consecutive_wires = Wires(range(len(tape.wires))) wires_map = OrderedDict(zip(tape.wires, consecutive_wires)) for operation in tape.operations: operation._wires = Wires([wires_map[wire] for wire in operation.wires.tolist()]) self.add_node(operation) self._add_successors() for obs in tape.observables: obs._wires = Wires([wires_map[wire] for wire in obs.wires.tolist()]) self.observables = tape.observables if tape.observables is not None else [] def _add_node(self, node): self.node_id += 1 node.node_id = self.node_id self._multi_graph.add_node(node.node_id, node=node)
[docs] def add_node(self, operation): """Add the operation as a node in the DAG and updates the edges. Args: operation (qml.operation): PennyLane quantum operation to add to the DAG. """ target_wires = [w for w in operation.wires if w not in operation.control_wires] new_node = CommutationDAGNode( op=operation, wires=operation.wires.tolist(), target_wires=target_wires, control_wires=operation.control_wires.tolist(), successors=[], predecessors=[], ) self._add_node(new_node) self._update_edges()
[docs] def get_node(self, node_id): """Add the operation as a node in the DAG and updates the edges. Args: node_id (int): PennyLane quantum operation to add to the DAG. Returns: CommutationDAGNOde: The node with the given id. """ return self._multi_graph.nodes(data="node")[node_id]
[docs] def get_nodes(self): """Return iterable to loop through all the nodes in the DAG. Returns: networkx.classes.reportviews.NodeDataView: Iterable nodes. """ return self._multi_graph.nodes(data="node")
[docs] def add_edge(self, node_in, node_out): """Add an edge (non commutation) between node_in and node_out. Args: node_in (int): Id of the ingoing node. node_out (int): Id of the outgoing node. Returns: int: Id of the created edge. """ return self._multi_graph.add_edge(node_in, node_out, commute=False)
[docs] def get_edge(self, node_in, node_out): """Get the edge between two nodes if it exists. Args: node_in (int): Id of the ingoing node. node_out (int): Id of the outgoing node. Returns: dict or None: Default weight is 0, it returns None when there is no edge. """ return self._multi_graph.get_edge_data(node_in, node_out)
[docs] def get_edges(self): """Get all edges as an iterable. Returns: networkx.classes.reportviews.OutMultiEdgeDataView: Iterable over all edges. """ return self._multi_graph.edges.data()
[docs] def direct_predecessors(self, node_id): """Return the direct predecessors of the given node. Args: node_id (int): Id of the node in the DAG. Returns: list[int]: List of the direct predecessors of the given node. """ dir_pred = list(self._multi_graph.pred[node_id].keys()) dir_pred.sort() return dir_pred
[docs] def predecessors(self, node_id): """Return the predecessors of the given node. Args: node_id (int): Id of the node in the DAG. Returns: list[int]: List of the predecessors of the given node. """ pred = list(nx.ancestors(self._multi_graph, node_id)) pred.sort() return pred
[docs] def direct_successors(self, node_id): """Return the direct successors of the given node. Args: node_id (int): Id of the node in the DAG. Returns: list[int]: List of the direct successors of the given node. """ dir_succ = list(self._multi_graph.succ[node_id].keys()) dir_succ.sort() return dir_succ
[docs] def successors(self, node_id): """Return the successors of the given node. Args: node_id (int): Id of the node in the DAG. Returns: list[int]: List of the successors of the given node. """ succ = list(nx.descendants(self._multi_graph, node_id)) succ.sort() return succ
@property def graph(self): """Return the DAG object. Returns: networkx.MultiDiGraph(): Networkx representation of the DAG. """ return self._multi_graph @property def size(self): """Return the size of the DAG object. Returns: int: Number of nodes in the DAG. """ return len(self._multi_graph) # pylint: disable=no-member
[docs] def draw(self, filename="dag.png"): # pragma: no cover """Draw the DAG object. Args: filename (str): The file name which is in PNG format. Default = 'dag.png' """ draw_graph = nx.MultiDiGraph() for node in self.get_nodes(): wires = ",".join([" " + str(elem) for elem in node[1].op.wires.tolist()]) label = ( "ID: " + str(node[0]) + "\n" + "Op: " + node[1].op.name + "\n" + "Wires: [" + wires[1::] + "]" ) draw_graph.add_node( node[0], label=label, color="blue", style="filled", fillcolor="lightblue" ) for edge in self.get_edges(): draw_graph.add_edge(edge[0], edge[1]) dot = to_pydot(draw_graph) dot.write_png(filename)
def _pred_update(self, node_id): self.get_node(node_id).predecessors = [] for d_pred in self.direct_predecessors(node_id): self.get_node(node_id).predecessors.append([d_pred]) self.get_node(node_id).predecessors.append(self.get_node(d_pred).predecessors) self.get_node(node_id).predecessors = list( _merge_no_duplicates(*self.get_node(node_id).predecessors) ) def _add_successors(self): for node_id in range(len(self._multi_graph) - 1, -1, -1): direct_successors = self.direct_successors(node_id) for d_succ in direct_successors: self.get_node(node_id).successors.append([d_succ]) self.get_node(node_id).successors.append(self.get_node(d_succ).successors) self.get_node(node_id).successors = list( _merge_no_duplicates(*self.get_node(node_id).successors) ) def _update_edges(self): max_node_id = len(self._multi_graph) - 1 max_node = self.get_node(max_node_id).op for current_node_id in range(0, max_node_id): self.get_node(current_node_id).reachable = True for prev_node_id in range(max_node_id - 1, -1, -1): if self.get_node(prev_node_id).reachable and not qml.is_commuting( self.get_node(prev_node_id).op, max_node ): self.add_edge(prev_node_id, max_node_id) self._pred_update(max_node_id) list_predecessors = self.get_node(max_node_id).predecessors for pred_id in list_predecessors: self.get_node(pred_id).reachable = False