qiskit.transpiler.passes.routing.sabre_swap의 소스 코드
# 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
# that they have been altered from the originals.
"""Routing via SWAP insertion using the SABRE method from Li et al."""
import logging
from copy import copy
from itertools import cycle
import numpy as np
from qiskit.circuit.library.standard_gates import SwapGate
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.transpiler.layout import Layout
from qiskit.dagcircuit import DAGNode
logger = logging.getLogger(__name__)
EXTENDED_SET_SIZE = 20 # Size of lookahead window. TODO: set dynamically to len(current_layout)
EXTENDED_SET_WEIGHT = 0.5 # Weight of lookahead window compared to front_layer.
DECAY_RATE = 0.001 # Decay cooefficient for penalizing serial swaps.
DECAY_RESET_INTERVAL = 5 # How often to reset all decay rates to 1.
[문서]class SabreSwap(TransformationPass):
r"""Map input circuit onto a backend topology via insertion of SWAPs.
Implementation of the SWAP-based heuristic search from the SABRE qubit
mapping paper [1] (Algorithm 1). The hueristic aims to minimize the number
of lossy SWAPs inserted and the depth of the circuit.
This algorithm starts from an initial layout of virtual qubits onto physical
qubits, and iterates over the circuit DAG until all gates are exhausted,
inserting SWAPs along the way. It only considers 2-qubit gates as only those
are germane for the mapping problem (it is assumed that 3+ qubit gates are
already decomposed).
In each iteration, it will first check if there are any gates in the
``front_layer`` that can be directly applied. If so, it will apply them and
remove them from ``front_layer``, and replenish that layer with new gates
if possible. Otherwise, it will try to search for SWAPs, insert the SWAPs,
and update the mapping.
The search for SWAPs is restricted, in the sense that we only consider
physical qubits in the neighoborhood of those qubits involved in
``front_layer``. These give rise to a ``swap_candidate_list`` which is
scored according to some heuristic cost function. The best SWAP is
implemented and ``current_layout`` updated.
**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>`_
"""
[문서] def __init__(self, coupling_map, heuristic='basic', seed=None):
r"""SabreSwap initializer.
Args:
coupling_map (CouplingMap): CouplingMap of the target backend.
heuristic (str): The type of heuristic to use when deciding best
swap strategy ('basic' or 'lookahead' or 'decay').
seed (int): random seed used to tie-break among candidate swaps.
Additional Information:
The search space of possible SWAPs on physical qubits is explored
by assigning a score to the layout that would result from each SWAP.
The goodness of a layout is evaluated based on how viable it makes
the remaining virtual gates that must be applied. A few heuristic
cost functions are supported
- 'basic':
The sum of distances for corresponding physical qubits of
interacting virtual qubits in the front_layer.
.. math::
H_{basic} = \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'lookahead':
This is the sum of two costs: first is the same as the basic cost.
Second is the basic cost but now evaluated for the
extended set as well (i.e. :math:`|E|` number of upcoming successors to gates in
front_layer F). This is weighted by some amount EXTENDED_SET_WEIGHT (W) to
signify that upcoming gates are less important that the front_layer.
.. math::
H_{decay}=\frac{1}{\left|{F}\right|}\sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]
+ W*\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
- 'decay':
This is the same as 'lookahead', but the whole cost is multiplied by a
decay factor. This increases the cost if the SWAP that generated the
trial layout was recently used (i.e. it penalizes increase in depth).
.. math::
H_{decay} = max(decay(SWAP.q_1), decay(SWAP.q_2)) {
\frac{1}{\left|{F}\right|} \sum_{gate \in F} D[\pi(gate.q_1)][\pi(gate.q2)]\\
+ W *\frac{1}{\left|{E}\right|} \sum_{gate \in E} D[\pi(gate.q_1)][\pi(gate.q2)]
}
"""
super().__init__()
self.coupling_map = coupling_map
self.heuristic = heuristic
self.seed = seed
self.applied_gates = None
self.qubits_decay = None
[문서] def run(self, dag):
"""Run the SabreSwap pass on `dag`.
Args:
dag (DAGCircuit): the directed acyclic graph to be mapped.
Returns:
DAGCircuit: A dag mapped to be compatible with the coupling_map.
Raises:
TranspilerError: if the coupling map or the layout are not
compatible with the DAG
"""
if len(dag.qregs) != 1 or dag.qregs.get('q', None) is None:
raise TranspilerError('Sabre swap runs on physical circuits only.')
if len(dag.qubits) > self.coupling_map.size():
raise TranspilerError('More virtual qubits exist than physical.')
rng = np.random.default_rng(self.seed)
# Preserve input DAG's name, regs, wire_map, etc. but replace the graph.
mapped_dag = dag._copy_circuit_metadata()
# Assume bidirectional couplings, fixing gate direction is easy later.
self.coupling_map.make_symmetric()
canonical_register = dag.qregs['q']
current_layout = Layout.generate_trivial_layout(canonical_register)
# A decay factor for each qubit used to heuristically penalize recently
# used qubits (to encourage parallelism).
self.qubits_decay = {qubit: 1 for qubit in dag.qubits}
# Start algorithm from the front layer and iterate until all gates done.
num_search_steps = 0
front_layer = dag.front_layer()
self.applied_gates = set()
while front_layer:
execute_gate_list = []
# Remove as many immediately applicable gates as possible
for node in front_layer:
if len(node.qargs) == 2:
v0, v1 = node.qargs
physical_qubits = (current_layout[v0], current_layout[v1])
if physical_qubits in self.coupling_map.get_edges():
execute_gate_list.append(node)
else: # Single-qubit gates as well as barriers are free
execute_gate_list.append(node)
if execute_gate_list:
for node in execute_gate_list:
new_node = _transform_gate_for_layout(node, current_layout)
mapped_dag.apply_operation_back(new_node.op,
new_node.qargs,
new_node.cargs,
new_node.condition)
front_layer.remove(node)
self.applied_gates.add(node)
for successor in dag.quantum_successors(node):
if successor.type != 'op':
continue
if self._is_resolved(successor, dag):
front_layer.append(successor)
if node.qargs:
self._reset_qubits_decay()
# Diagnostics
logger.debug('free! %s',
[(n.name, n.qargs) for n in execute_gate_list])
logger.debug('front_layer: %s',
[(n.name, n.qargs) for n in front_layer])
continue
# After all free gates are exhausted, heuristically find
# the best swap and insert it. When two or more swaps tie
# for best score, pick one randomly.
extended_set = self._obtain_extended_set(dag, front_layer)
swap_candidates = self._obtain_swaps(front_layer, current_layout)
swap_scores = dict.fromkeys(swap_candidates, 0)
for swap_qubits in swap_scores:
trial_layout = current_layout.copy()
trial_layout.swap(*swap_qubits)
score = self._score_heuristic(self.heuristic,
front_layer,
extended_set,
trial_layout,
swap_qubits)
swap_scores[swap_qubits] = score
min_score = min(swap_scores.values())
best_swaps = [k for k, v in swap_scores.items() if v == min_score]
best_swaps.sort(key=lambda x: (x[0].index, x[1].index))
best_swap = rng.choice(best_swaps)
swap_node = DAGNode(op=SwapGate(), qargs=best_swap, type='op')
swap_node = _transform_gate_for_layout(swap_node, current_layout)
mapped_dag.apply_operation_back(swap_node.op, swap_node.qargs)
current_layout.swap(*best_swap)
num_search_steps += 1
if num_search_steps % DECAY_RESET_INTERVAL == 0:
self._reset_qubits_decay()
else:
self.qubits_decay[best_swap[0]] += DECAY_RATE
self.qubits_decay[best_swap[1]] += DECAY_RATE
# Diagnostics
logger.debug('SWAP Selection...')
logger.debug('extended_set: %s',
[(n.name, n.qargs) for n in extended_set])
logger.debug('swap scores: %s', swap_scores)
logger.debug('best swap: %s', best_swap)
logger.debug('qubits decay: %s', self.qubits_decay)
self.property_set['final_layout'] = current_layout
return mapped_dag
def _reset_qubits_decay(self):
"""Reset all qubit decay factors to 1 upon request (to forget about
past penalizations).
"""
self.qubits_decay = {k: 1 for k in self.qubits_decay.keys()}
def _is_resolved(self, node, dag):
"""Return True if all of a node's predecessors in dag are applied.
"""
predecessors = dag.quantum_predecessors(node)
predecessors = filter(lambda x: x.type == 'op', predecessors)
return all([n in self.applied_gates for n in predecessors])
def _obtain_extended_set(self, dag, front_layer):
"""Populate extended_set by looking ahead a fixed number of gates.
For each existing element add a successor until reaching limit.
"""
# TODO: use layers instead of bfs_successors so long range successors aren't included.
extended_set = set()
bfs_successors_pernode = [dag.bfs_successors(n) for n in front_layer]
node_lookahead_exhausted = [False] * len(front_layer)
for i, node_successor_generator in cycle(enumerate(bfs_successors_pernode)):
if all(node_lookahead_exhausted) or len(extended_set) >= EXTENDED_SET_SIZE:
break
try:
_, successors = next(node_successor_generator)
successors = list(filter(lambda x: x.type == 'op' and len(x.qargs) == 2,
successors))
except StopIteration:
node_lookahead_exhausted[i] = True
continue
successors = iter(successors)
while len(extended_set) < EXTENDED_SET_SIZE:
try:
extended_set.add(next(successors))
except StopIteration:
break
return extended_set
def _obtain_swaps(self, front_layer, current_layout):
"""Return a set of candidate swaps that affect qubits in front_layer.
For each virtual qubit in front_layer, find its current location
on hardware and the physical qubits in that neighborhood. Every SWAP
on virtual qubits that corresponds to one of those physical couplings
is a candidate SWAP.
Candidate swaps are sorted so SWAP(i,j) and SWAP(j,i) are not duplicated.
"""
candidate_swaps = set()
for node in front_layer:
for virtual in node.qargs:
physical = current_layout[virtual]
for neighbor in self.coupling_map.neighbors(physical):
virtual_neighbor = current_layout[neighbor]
swap = sorted([virtual, virtual_neighbor],
key=lambda q: (q.register.name, q.index))
candidate_swaps.add(tuple(swap))
return candidate_swaps
def _score_heuristic(self, heuristic, front_layer, extended_set, layout, swap_qubits=None):
"""Return a heuristic score for a trial layout.
Assuming a trial layout has resulted from a SWAP, we now assign a cost
to it. The goodness of a layout is evaluated based on how viable it makes
the remaining virtual gates that must be applied.
"""
if heuristic == 'basic':
return sum(self.coupling_map.distance(*[layout[q] for q in node.qargs])
for node in front_layer)
elif heuristic == 'lookahead':
first_cost = self._score_heuristic('basic', front_layer, [], layout)
first_cost /= len(front_layer)
second_cost = self._score_heuristic('basic', extended_set, [], layout)
second_cost = 0.0 if not extended_set else second_cost / len(extended_set)
return first_cost + EXTENDED_SET_WEIGHT * second_cost
elif heuristic == 'decay':
return max(self.qubits_decay[swap_qubits[0]], self.qubits_decay[swap_qubits[1]]) * \
self._score_heuristic('lookahead', front_layer, extended_set, layout)
else:
raise TranspilerError('Heuristic %s not recognized.' % heuristic)
def _transform_gate_for_layout(op_node, layout):
"""Return node implementing a virtual op on given layout."""
mapped_op_node = copy(op_node)
device_qreg = op_node.qargs[0].register
premap_qargs = op_node.qargs
mapped_qargs = map(lambda x: device_qreg[layout[x]], premap_qargs)
mapped_op_node.qargs = list(mapped_qargs)
return mapped_op_node