C贸digo fuente para qiskit.transpiler.passes.routing.stochastic_swap

# This code is part of Qiskit.
# (C) Copyright IBM 2017, 2018.
# 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.

"""Map a DAGCircuit onto a `coupling_map` adding swap gates."""

import itertools
import logging
from math import inf
import numpy as np

from qiskit.converters import dag_to_circuit, circuit_to_dag
from qiskit.circuit.classical import expr, types
from qiskit.circuit.quantumregister import QuantumRegister
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.dagcircuit import DAGCircuit
from qiskit.circuit.library.standard_gates import SwapGate
from qiskit.transpiler.layout import Layout
from qiskit.transpiler.target import Target
from qiskit.circuit import (
from qiskit._accelerate import stochastic_swap as stochastic_swap_rs
from qiskit._accelerate import nlayout
from qiskit.transpiler.passes.layout import disjoint_utils

from .utils import get_swap_map_dag

logger = logging.getLogger(__name__)

[documentos]class StochasticSwap(TransformationPass): """Map a DAGCircuit onto a `coupling_map` adding swap gates. Uses a randomized algorithm. Notes: 1. Measurements may occur and be followed by swaps that result in repeated measurement of the same qubit. Near-term experiments cannot implement these circuits, so some care is required when using this mapper with experimental backend targets. 2. We do not use the fact that the input state is zero to simplify the circuit. """ def __init__(self, coupling_map, trials=20, seed=None, fake_run=False, initial_layout=None): """StochasticSwap initializer. The coupling map is a connected graph If these are not satisfied, the behavior is undefined. Args: coupling_map (Union[CouplingMap, Target]): Directed graph representing a coupling map. trials (int): maximum number of iterations to attempt seed (int): seed for random number generator fake_run (bool): if true, it only pretend to do routing, i.e., no swap is effectively added. initial_layout (Layout): starting layout at beginning of pass. """ super().__init__() if isinstance(coupling_map, Target): self.target = coupling_map self.coupling_map = self.target.build_coupling_map() else: self.target = None self.coupling_map = coupling_map self.trials = trials self.seed = seed self.rng = None self.fake_run = fake_run self.qregs = None self.initial_layout = initial_layout self._int_to_qubit = None
[documentos] def run(self, dag): """Run the StochasticSwap pass on `dag`. Args: dag (DAGCircuit): DAG to map. Returns: DAGCircuit: A mapped DAG. Raises: TranspilerError: if the coupling map or the layout are not compatible with the DAG, or if the coupling_map=None """ if self.coupling_map is None: raise TranspilerError("StochasticSwap cannot run with coupling_map=None") if len(dag.qregs) != 1 or dag.qregs.get("q", None) is None: raise TranspilerError("StochasticSwap runs on physical circuits only") if len(dag.qubits) > len(self.coupling_map.physical_qubits): raise TranspilerError("The layout does not match the amount of qubits in the DAG") disjoint_utils.require_layout_isolated_to_component( dag, self.coupling_map if self.target is None else self.target ) self.rng = np.random.default_rng(self.seed) canonical_register = dag.qregs["q"] if self.initial_layout is None: self.initial_layout = Layout.generate_trivial_layout(canonical_register) # Qubit indices are used to assign an integer to each virtual qubit during the routing: it's # a mapping of {virtual: virtual}, for converting between Python and Rust forms. self._int_to_qubit = tuple(dag.qubits) self.qregs = dag.qregs logger.debug("StochasticSwap rng seeded with seed=%s", self.seed) self.coupling_map.compute_distance_matrix() new_dag = self._mapper(dag, self.coupling_map, trials=self.trials) return new_dag
def _layer_permutation(self, dag, layer_partition, layout, qubit_subset, coupling, trials): """Find a swap circuit that implements a permutation for this layer. The goal is to swap qubits such that qubits in the same two-qubit gates are adjacent. Based on S. Bravyi's algorithm. Args: layer_partition (list): The layer_partition is a list of (qu)bit lists and each qubit is a tuple (qreg, index). layout (Layout): The layout is a Layout object mapping virtual qubits in the input circuit to physical qubits in the coupling graph. It reflects the current positions of the data. qubit_subset (list): The qubit_subset is the set of qubits in the coupling graph that we have chosen to map into, as tuples (Register, index). coupling (CouplingMap): Directed graph representing a coupling map. This coupling map should be one that was provided to the stochastic mapper. trials (int): Number of attempts the randomized algorithm makes. Returns: Tuple: success_flag, best_circuit, best_depth, best_layout If success_flag is True, then best_circuit contains a DAGCircuit with the swap circuit, best_depth contains the depth of the swap circuit, and best_layout contains the new positions of the data qubits after the swap circuit has been applied. Raises: TranspilerError: if anything went wrong. """ logger.debug("layer_permutation: layer_partition = %s", layer_partition) logger.debug("layer_permutation: layout = %s", layout.get_virtual_bits()) logger.debug("layer_permutation: qubit_subset = %s", qubit_subset) logger.debug("layer_permutation: trials = %s", trials) # The input dag is on a flat canonical register canonical_register = QuantumRegister(len(layout), "q") gates = [] # list of lists of tuples [[(register, index), ...], ...] for gate_args in layer_partition: if len(gate_args) > 2: raise TranspilerError("Layer contains > 2-qubit gates") if len(gate_args) == 2: gates.append(tuple(gate_args)) logger.debug("layer_permutation: gates = %s", gates) # Can we already apply the gates? If so, there is no work to do. # Accessing via private attributes to avoid overhead from __getitem__ # and to optimize performance of the distance matrix access dist = sum(coupling._dist_matrix[layout._v2p[g[0]], layout._v2p[g[1]]] for g in gates) logger.debug("layer_permutation: distance = %s", dist) if dist == len(gates): logger.debug("layer_permutation: nothing to do") circ = DAGCircuit() circ.add_qreg(canonical_register) return True, circ, 0, layout # Begin loop over trials of randomized algorithm num_qubits = len(layout) best_depth = inf # initialize best depth best_edges = None # best edges found best_circuit = None # initialize best swap circuit best_layout = None # initialize best final layout cdist2 = coupling._dist_matrix**2 int_qubit_subset = np.fromiter( (dag.find_bit(bit).index for bit in qubit_subset), dtype=np.uintp, count=len(qubit_subset), ) int_gates = np.fromiter( (dag.find_bit(bit).index for gate in gates for bit in gate), dtype=np.uintp, count=2 * len(gates), ) layout_mapping = {dag.find_bit(k).index: v for k, v in layout.get_virtual_bits().items()} int_layout = nlayout.NLayout(layout_mapping, num_qubits, coupling.size()) trial_circuit = DAGCircuit() # SWAP circuit for slice of swaps in this trial trial_circuit.add_qubits(layout.get_virtual_bits()) edges = np.asarray(coupling.get_edges(), dtype=np.uintp).ravel() cdist = coupling._dist_matrix best_edges, best_layout, best_depth = stochastic_swap_rs.swap_trials( trials, num_qubits, int_layout, int_qubit_subset, int_gates, cdist, cdist2, edges, seed=self.seed, ) # If we have no best circuit for this layer, all of the trials have failed if best_layout is None: logger.debug("layer_permutation: failed!") return False, None, None, None edges = best_edges.edges() for idx in range(len(edges) // 2): swap_src = self._int_to_qubit[edges[2 * idx]] swap_tgt = self._int_to_qubit[edges[2 * idx + 1]] trial_circuit.apply_operation_back(SwapGate(), [swap_src, swap_tgt], []) best_circuit = trial_circuit # Otherwise, we return our result for this layer logger.debug("layer_permutation: success!") layout_mapping = best_layout.layout_mapping() best_lay = Layout({best_circuit.qubits[k]: v for (k, v) in layout_mapping}) return True, best_circuit, best_depth, best_lay def _layer_update(self, dag, layer, best_layout, best_depth, best_circuit): """Add swaps followed by the now mapped layer from the original circuit. Args: dag (DAGCircuit): The DAGCircuit object that the _mapper method is building layer (DAGCircuit): A DAGCircuit layer from the original circuit best_layout (Layout): layout returned from _layer_permutation best_depth (int): depth returned from _layer_permutation best_circuit (DAGCircuit): swap circuit returned from _layer_permutation """ logger.debug("layer_update: layout = %s", best_layout) logger.debug("layer_update: self.initial_layout = %s", self.initial_layout) # Output any swaps if best_depth > 0: logger.debug("layer_update: there are swaps in this layer, depth %d", best_depth) dag.compose(best_circuit, qubits={bit: bit for bit in best_circuit.qubits}) else: logger.debug("layer_update: there are no swaps in this layer") # Output this layer dag.compose(layer["graph"], qubits=best_layout.reorder_bits(dag.qubits)) def _mapper(self, circuit_graph, coupling_graph, trials=20): """Map a DAGCircuit onto a CouplingMap using swap gates. Args: circuit_graph (DAGCircuit): input DAG circuit coupling_graph (CouplingMap): coupling graph to map onto trials (int): number of trials. Returns: DAGCircuit: object containing a circuit equivalent to circuit_graph that respects couplings in coupling_graph Raises: TranspilerError: if there was any error during the mapping or with the parameters. """ # Schedule the input circuit by calling layers() layerlist = list(circuit_graph.layers()) logger.debug("schedule:") for i, v in enumerate(layerlist): logger.debug(" %d: %s", i, v["partition"]) qubit_subset = self.initial_layout.get_virtual_bits().keys() # Find swap circuit to precede each layer of input circuit layout = self.initial_layout.copy() # Construct an empty DAGCircuit with the same set of # qregs and cregs as the input circuit dagcircuit_output = None if not self.fake_run: dagcircuit_output = circuit_graph.copy_empty_like() logger.debug("layout = %s", layout) # Iterate over layers for i, layer in enumerate(layerlist): # First try and compute a route for the entire layer in one go. if not layer["graph"].op_nodes(op=ControlFlowOp): success_flag, best_circuit, best_depth, best_layout = self._layer_permutation( circuit_graph, layer["partition"], layout, qubit_subset, coupling_graph, trials ) logger.debug("mapper: layer %d", i) logger.debug("mapper: success_flag=%s,best_depth=%s", success_flag, str(best_depth)) if success_flag: layout = best_layout # Update the DAG if not self.fake_run: self._layer_update( dagcircuit_output, layerlist[i], best_layout, best_depth, best_circuit ) continue # If we're here, we need to go through every gate in the layer serially. logger.debug("mapper: failed, layer %d, retrying sequentially", i) # Go through each gate in the layer for j, serial_layer in enumerate(layer["graph"].serial_layers()): layer_dag = serial_layer["graph"] # layer_dag has only one operation op_node = layer_dag.op_nodes()[0] if isinstance(op_node.op, ControlFlowOp): layout = self._controlflow_layer_update( dagcircuit_output, layer_dag, layout, circuit_graph ) else: (success_flag, best_circuit, best_depth, best_layout) = self._layer_permutation( circuit_graph, serial_layer["partition"], layout, qubit_subset, coupling_graph, trials, ) logger.debug("mapper: layer %d, sublayer %d", i, j) logger.debug( "mapper: success_flag=%s,best_depth=%s,", success_flag, str(best_depth) ) # Give up if we fail again if not success_flag: raise TranspilerError( "swap mapper failed: " + "layer %d, sublayer %d" % (i, j) ) # Update the record of qubit positions # for each inner iteration layout = best_layout # Update the DAG if not self.fake_run: self._layer_update( dagcircuit_output, serial_layer, best_layout, best_depth, best_circuit, ) # This is the final edgemap. We might use it to correctly replace # any measurements that needed to be removed earlier. logger.debug("mapper: self.initial_layout = %s", self.initial_layout) logger.debug("mapper: layout = %s", layout) self.property_set["final_layout"] = layout if self.fake_run: return circuit_graph return dagcircuit_output def _controlflow_layer_update(self, dagcircuit_output, layer_dag, current_layout, root_dag): """ Updates the new dagcircuit with a routed control flow operation. Args: dagcircuit_output (DAGCircuit): dagcircuit that is being built with routed operations. layer_dag (DAGCircuit): layer to route containing a single controlflow operation. current_layout (Layout): current layout coming into this layer. root_dag (DAGCircuit): root dag of pass Returns: Layout: updated layout after this layer has been routed. Raises: TranspilerError: if layer_dag does not contain a recognized ControlFlowOp. """ node = layer_dag.op_nodes()[0] if not isinstance(node.op, (IfElseOp, ForLoopOp, WhileLoopOp, SwitchCaseOp)): raise TranspilerError(f"unsupported control flow operation: {node}") # For each block, expand it up be the full width of the containing DAG so we can be certain # that it is routable, then route it within that. When we recombine later, we'll reduce all # these blocks down to remove any qubits that are idle. block_dags = [] block_layouts = [] for block in node.op.blocks: inner_pass = self._recursive_pass(current_layout) block_dags.append(inner_pass.run(_dag_from_block(block, node, root_dag))) block_layouts.append(inner_pass.property_set["final_layout"].copy()) # Determine what layout we need to go towards. For some blocks (such as `for`), we must # guarantee that the final layout is the same as the initial or the loop won't work. if _controlflow_exhaustive_acyclic(node.op): # We heuristically just choose to use the layout of whatever the deepest block is, to # avoid extending the total depth by too much. final_layout = max( zip(block_layouts, block_dags), key=lambda x: x[1].depth(recurse=True) )[0] else: final_layout = current_layout if self.fake_run: return final_layout # Add swaps to the end of each block to make sure they all have the same layout at the end. # Adding these swaps can cause fewer wires to be idle than we expect (if we have to swap # across unused qubits), so we track that at this point too. idle_qubits = set(root_dag.qubits) for layout, updated_dag_block in zip(block_layouts, block_dags): swap_dag, swap_qubits = get_swap_map_dag( root_dag, self.coupling_map, layout, final_layout, seed=self._new_seed() ) if swap_dag.size(recurse=False): updated_dag_block.compose(swap_dag, qubits=swap_qubits) idle_qubits &= set(updated_dag_block.idle_wires()) # Now for each block, expand it to be full width over all active wires (all blocks of a # control-flow operation need to have equal input wires), and convert it to circuit form. block_circuits = [] for updated_dag_block in block_dags: updated_dag_block.remove_qubits(*idle_qubits) block_circuits.append(dag_to_circuit(updated_dag_block)) new_op = node.op.replace_blocks(block_circuits) new_qargs = block_circuits[0].qubits dagcircuit_output.apply_operation_back(new_op, new_qargs, node.cargs) return final_layout def _new_seed(self): """Get a seed for a new RNG instance.""" return self.rng.integers(0x7FFF_FFFF_FFFF_FFFF) def _recursive_pass(self, initial_layout): """Get a new instance of this class to handle a recursive call for a control-flow block. Each pass starts with its own new seed, determined deterministically from our own.""" return self.__class__( self.coupling_map, # This doesn't cause an exponential explosion of the trials because we only generate a # recursive pass instance for control-flow operations, while the trial multiplicity is # only for non-control-flow layers. trials=self.trials, seed=self._new_seed(), fake_run=self.fake_run, initial_layout=initial_layout, )
def _controlflow_exhaustive_acyclic(operation: ControlFlowOp): """Return True if the entire control-flow operation represents a block that is guaranteed to be entered, and does not cycle back to the initial layout.""" if isinstance(operation, IfElseOp): return len(operation.blocks) == 2 if isinstance(operation, SwitchCaseOp): cases = operation.cases() if isinstance(operation.target, expr.Expr): type_ = operation.target.type if type_.kind is types.Bool: max_matches = 2 elif type_.kind is types.Uint: max_matches = 1 << type_.width else: raise RuntimeError(f"unhandled target type: '{type_}'") else: max_matches = 2 if isinstance(operation.target, Clbit) else 1 << len(operation.target) return CASE_DEFAULT in cases or len(cases) == max_matches return False def _dag_from_block(block, node, root_dag): """Get a :class:`DAGCircuit` that represents the :class:`.QuantumCircuit` ``block`` embedded within the ``root_dag`` for full-width routing purposes. This means that all the qubits are in the output DAG, but only the necessary clbits and classical registers are.""" out = DAGCircuit() # The pass already ensured that `root_dag` has only a single quantum register with everything. for qreg in root_dag.qregs.values(): out.add_qreg(qreg) # For clbits, we need to take more care. Nested control-flow might need registers to exist for # conditions on inner blocks. `DAGCircuit.substitute_node_with_dag` handles this register # mapping when required, so we use that with a dummy block. out.add_clbits(node.cargs) dummy = out.apply_operation_back( Instruction("dummy", len(node.qargs), len(node.cargs), []), node.qargs, node.cargs ) wire_map = dict(itertools.chain(zip(block.qubits, node.qargs), zip(block.clbits, node.cargs))) out.substitute_node_with_dag(dummy, circuit_to_dag(block), wires=wire_map) return out