qiskit.transpiler.passes.layout.sabre_layout のソースコード

# This code is part of Qiskit.
# (C) Copyright IBM 2017, 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
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"""Layout selection using the SABRE bidirectional search approach from Li et al.

import copy
import logging
import numpy as np
import rustworkx as rx

from qiskit.converters import dag_to_circuit
from qiskit.transpiler.passes.layout.set_layout import SetLayout
from qiskit.transpiler.passes.layout.full_ancilla_allocation import FullAncillaAllocation
from qiskit.transpiler.passes.layout.enlarge_with_ancilla import EnlargeWithAncilla
from qiskit.transpiler.passes.layout.apply_layout import ApplyLayout
from qiskit.transpiler.passmanager import PassManager
from qiskit.transpiler.layout import Layout
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.transpiler.exceptions import TranspilerError
from qiskit._accelerate.nlayout import NLayout
from qiskit._accelerate.sabre_layout import sabre_layout_and_routing
from qiskit._accelerate.sabre_swap import (
from qiskit.transpiler.passes.routing.sabre_swap import process_swaps, apply_gate
from qiskit.tools.parallel import CPU_COUNT

logger = logging.getLogger(__name__)

[ドキュメント]class SabreLayout(TransformationPass): """Choose a Layout via iterative bidirectional routing of the input circuit. Starting with a random initial `Layout`, the algorithm does a full routing of the circuit (via the `routing_pass` method) to end up with a `final_layout`. This final_layout is then used as the initial_layout for routing the reverse circuit. The algorithm iterates a number of times until it finds an initial_layout that reduces full routing cost. This method exploits the reversibility of quantum circuits, and tries to include global circuit information in the choice of initial_layout. By default this pass will run both layout and routing and will transform the circuit so that the layout is applied to the input dag (meaning that the output circuit will have ancilla qubits allocated for unused qubits on the coupling map and the qubits will be reordered to match the mapped physical qubits) and then routing will be applied (inserting :class:`~.SwapGate`s to account for limited connectivity). This is unlike most other layout passes which are :class:`~.AnalysisPass` objects and just find an initial layout and set that on the property set. This is done because by default the pass will run parallel seed trials with different random seeds for selecting the random initial layout and then selecting the routed output which results in the least number of swap gates needed. You can use the ``routing_pass`` argument to have this pass operate as a typical layout pass. When specified this will use the specified routing pass to select an initial layout only and will not run multiple seed trials. **References:** [1] Li, Gushu, Yufei Ding, and Yuan Xie. "Tackling the qubit mapping problem for NISQ-era quantum devices." ASPLOS 2019. `arXiv:1809.02573 <https://arxiv.org/pdf/1809.02573.pdf>`_ """ def __init__( self, coupling_map, routing_pass=None, seed=None, max_iterations=3, swap_trials=None, layout_trials=None, skip_routing=False, ): """SabreLayout initializer. Args: coupling_map (Coupling): directed graph representing a coupling map. routing_pass (BasePass): the routing pass to use while iterating. If specified this pass operates as an :class:`~.AnalysisPass` and will only populate the ``layout`` field in the property set and the input dag is returned unmodified. This argument is mutually exclusive with the ``swap_trials`` and the ``layout_trials`` arguments and if this is specified at the same time as either argument an error will be raised. seed (int): seed for setting a random first trial layout. max_iterations (int): number of forward-backward iterations. swap_trials (int): The number of trials to run of :class:`~.SabreSwap` for each iteration. This is equivalent to the ``trials`` argument on :class:`~.SabreSwap`. If this is not specified (and ``routing_pass`` isn't set) by default the number of physical CPUs on your local system will be used. For reproducibility between environments it is best to set this to an explicit number because the output will potentially depend on the number of trials run. This option is mutually exclusive with the ``routing_pass`` argument and an error will be raised if both are used. layout_trials (int): The number of random seed trials to run layout with. When > 1 the trial that resuls in the output with the fewest swap gates will be selected. If this is not specified (and ``routing_pass`` is not set) then the number of local physical CPUs will be used as the default value. This option is mutually exclusive with the ``routing_pass`` argument and an error will be raised if both are used. skip_routing (bool): If this is set ``True`` and ``routing_pass`` is not used then routing will not be applied to the output circuit. Only the layout will be returned in the property set. This is a tradeoff to run custom routing with multiple layout trials, as using this option will cause SabreLayout to run the routing stage internally but not use that result. Raises: TranspilerError: If both ``routing_pass`` and ``swap_trials`` or both ``routing_pass`` and ``layout_trials`` are specified """ super().__init__() self.coupling_map = coupling_map self._neighbor_table = None if self.coupling_map is not None: if not self.coupling_map.is_symmetric: # deepcopy is needed here to avoid modifications updating # shared references in passes which require directional # constraints self.coupling_map = copy.deepcopy(self.coupling_map) self.coupling_map.make_symmetric() self._neighbor_table = NeighborTable(rx.adjacency_matrix(self.coupling_map.graph)) if routing_pass is not None and (swap_trials is not None or layout_trials is not None): raise TranspilerError("Both routing_pass and swap_trials can't be set at the same time") self.routing_pass = routing_pass self.seed = seed self.max_iterations = max_iterations self.trials = swap_trials if swap_trials is None: self.swap_trials = CPU_COUNT else: self.swap_trials = swap_trials if layout_trials is None: self.layout_trials = CPU_COUNT else: self.layout_trials = layout_trials self.skip_routing = skip_routing
[ドキュメント] def run(self, dag): """Run the SabreLayout pass on `dag`. Args: dag (DAGCircuit): DAG to find layout for. Returns: DAGCircuit: The output dag if swap mapping was run (otherwise the input dag is returned unmodified). Raises: TranspilerError: if dag wider than self.coupling_map """ if len(dag.qubits) > self.coupling_map.size(): raise TranspilerError("More virtual qubits exist than physical.") # Choose a random initial_layout. if self.routing_pass is not None: if self.seed is None: seed = np.random.randint(0, np.iinfo(np.int32).max) else: seed = self.seed rng = np.random.default_rng(seed) physical_qubits = rng.choice(self.coupling_map.size(), len(dag.qubits), replace=False) physical_qubits = rng.permutation(physical_qubits) initial_layout = Layout({q: dag.qubits[i] for i, q in enumerate(physical_qubits)}) self.routing_pass.fake_run = True # Do forward-backward iterations. circ = dag_to_circuit(dag) rev_circ = circ.reverse_ops() for _ in range(self.max_iterations): for _ in ("forward", "backward"): pm = self._layout_and_route_passmanager(initial_layout) new_circ = pm.run(circ) # Update initial layout and reverse the unmapped circuit. pass_final_layout = pm.property_set["final_layout"] final_layout = self._compose_layouts( initial_layout, pass_final_layout, new_circ.qregs ) initial_layout = final_layout circ, rev_circ = rev_circ, circ # Diagnostics logger.info("new initial layout") logger.info(initial_layout) for qreg in dag.qregs.values(): initial_layout.add_register(qreg) self.property_set["layout"] = initial_layout self.routing_pass.fake_run = False return dag dist_matrix = self.coupling_map.distance_matrix original_qubit_indices = {bit: index for index, bit in enumerate(dag.qubits)} original_clbit_indices = {bit: index for index, bit in enumerate(dag.clbits)} dag_list = [] for node in dag.topological_op_nodes(): cargs = {original_clbit_indices[x] for x in node.cargs} if node.op.condition is not None: for clbit in dag._bits_in_condition(node.op.condition): cargs.add(original_clbit_indices[clbit]) dag_list.append( ( node._node_id, [original_qubit_indices[x] for x in node.qargs], cargs, ) ) ((initial_layout, final_layout), swap_map, gate_order) = sabre_layout_and_routing( len(dag.clbits), dag_list, self._neighbor_table, dist_matrix, Heuristic.Decay, self.max_iterations, self.swap_trials, self.layout_trials, self.seed, ) # Apply initial layout selected. original_dag = dag layout_dict = {} num_qubits = len(dag.qubits) for k, v in initial_layout.layout_mapping(): if k < num_qubits: layout_dict[dag.qubits[k]] = v initital_layout = Layout(layout_dict) self.property_set["layout"] = initital_layout # If skip_routing is set then return the layout in the property set # and throwaway the extra work we did to compute the swap map if self.skip_routing: return dag # After this point the pass is no longer an analysis pass and the # output circuit returned is transformed with the layout applied # and swaps inserted dag = self._apply_layout_no_pass_manager(dag) # Apply sabre swap ontop of circuit with sabre layout final_layout_mapping = final_layout.layout_mapping() self.property_set["final_layout"] = Layout( {dag.qubits[k]: v for (k, v) in final_layout_mapping} ) mapped_dag = dag.copy_empty_like() canonical_register = dag.qregs["q"] qubit_indices = {bit: idx for idx, bit in enumerate(canonical_register)} original_layout = NLayout.generate_trivial_layout(self.coupling_map.size()) for node_id in gate_order: node = original_dag._multi_graph[node_id] process_swaps( swap_map, node, mapped_dag, original_layout, canonical_register, False, qubit_indices, ) apply_gate(mapped_dag, node, original_layout, canonical_register, False, layout_dict) return mapped_dag
def _apply_layout_no_pass_manager(self, dag): """Apply and embed a layout into a dagcircuit without using a ``PassManager`` to avoid circuit<->dag conversion. """ ancilla_pass = FullAncillaAllocation(self.coupling_map) ancilla_pass.property_set = self.property_set dag = ancilla_pass.run(dag) enlarge_pass = EnlargeWithAncilla() enlarge_pass.property_set = ancilla_pass.property_set dag = enlarge_pass.run(dag) apply_pass = ApplyLayout() apply_pass.property_set = enlarge_pass.property_set dag = apply_pass.run(dag) return dag def _layout_and_route_passmanager(self, initial_layout): """Return a passmanager for a full layout and routing. We use a factory to remove potential statefulness of passes. """ layout_and_route = [ SetLayout(initial_layout), FullAncillaAllocation(self.coupling_map), EnlargeWithAncilla(), ApplyLayout(), self.routing_pass, ] pm = PassManager(layout_and_route) return pm def _compose_layouts(self, initial_layout, pass_final_layout, qregs): """Return the real final_layout resulting from the composition of an initial_layout with the final_layout reported by a pass. The routing passes internally start with a trivial layout, as the layout gets applied to the circuit prior to running them. So the "final_layout" they report must be amended to account for the actual initial_layout that was selected. """ trivial_layout = Layout.generate_trivial_layout(*qregs) qubit_map = Layout.combine_into_edge_map(initial_layout, trivial_layout) final_layout = {v: pass_final_layout._v2p[qubit_map[v]] for v in initial_layout._v2p} return Layout(final_layout)