Code source de qiskit.transpiler.passes.layout.dense_layout

# 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
# Any modifications or derivative works of this code must retain this
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"""Choose a Layout by finding the most connected subset of qubits."""

import numpy as np
import rustworkx

from qiskit.transpiler.layout import Layout
from qiskit.transpiler.basepasses import AnalysisPass
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.transpiler.passes.layout import disjoint_utils

from qiskit._accelerate.dense_layout import best_subset

[docs]class DenseLayout(AnalysisPass): """Choose a Layout by finding the most connected subset of qubits. This pass associates a physical qubit (int) to each virtual qubit of the circuit (Qubit). Note: Even though a 'layout' is not strictly a property of the DAG, in the transpiler architecture it is best passed around between passes by being set in `property_set`. """ def __init__(self, coupling_map=None, backend_prop=None, target=None): """DenseLayout initializer. Args: coupling_map (Coupling): directed graph representing a coupling map. backend_prop (BackendProperties): backend properties object target (Target): A target representing the target backend. """ super().__init__() self.coupling_map = coupling_map self.backend_prop = backend_prop = target self.adjacency_matrix = None if target is not None: self.coupling_map = target.build_coupling_map()
[docs] def run(self, dag): """Run the DenseLayout pass on `dag`. Pick a convenient layout depending on the best matching qubit connectivity, and set the property `layout`. Args: dag (DAGCircuit): DAG to find layout for. Raises: TranspilerError: if dag wider than self.coupling_map """ if self.coupling_map is None: raise TranspilerError( "A coupling_map or target with constrained qargs is necessary to run the pass." ) layout_components = disjoint_utils.run_pass_over_connected_components( dag, self.coupling_map if is None else, self._inner_run, ) layout_mapping = {} for component in layout_components: layout_mapping.update(component) layout = Layout(layout_mapping) for qreg in dag.qregs.values(): layout.add_register(qreg) self.property_set["layout"] = layout
def _inner_run(self, dag, coupling_map): num_dag_qubits = len(dag.qubits) if num_dag_qubits > coupling_map.size(): raise TranspilerError("Number of qubits greater than device.") num_cx = 0 num_meas = 0 if is not None: num_cx = 1 num_meas = 1 else: # Get avg number of cx and meas per qubit ops = dag.count_ops(recurse=True) if "cx" in ops.keys(): num_cx = ops["cx"] if "measure" in ops.keys(): num_meas = ops["measure"] best_sub = self._best_subset(num_dag_qubits, num_meas, num_cx, coupling_map) layout_mapping = { qubit: coupling_map.graph[int(best_sub[i])] for i, qubit in enumerate(dag.qubits) } return layout_mapping def _best_subset(self, num_qubits, num_meas, num_cx, coupling_map): """Computes the qubit mapping with the best connectivity. Args: num_qubits (int): Number of subset qubits to consider. Returns: ndarray: Array of qubits to use for best connectivity mapping. """ from scipy.sparse import coo_matrix, csgraph if num_qubits == 1: return np.array([0]) if num_qubits == 0: return [] adjacency_matrix = rustworkx.adjacency_matrix(coupling_map.graph) reverse_index_map = {v: k for k, v in enumerate(coupling_map.graph.nodes())} error_mat, use_error = _build_error_matrix( coupling_map.size(), reverse_index_map, backend_prop=self.backend_prop, coupling_map=self.coupling_map,, ) rows, cols, best_map = best_subset( num_qubits, adjacency_matrix, num_meas, num_cx, use_error, coupling_map.is_symmetric, error_mat, ) data = [1] * len(rows) sp_sub_graph = coo_matrix((data, (rows, cols)), shape=(num_qubits, num_qubits)).tocsr() perm = csgraph.reverse_cuthill_mckee(sp_sub_graph) best_map = best_map[perm] return best_map
def _build_error_matrix(num_qubits, qubit_map, target=None, coupling_map=None, backend_prop=None): error_mat = np.zeros((num_qubits, num_qubits)) use_error = False if target is not None and target.qargs is not None: for qargs in target.qargs: # Ignore gates over 2q DenseLayout only works with 2q if len(qargs) > 2: continue error = 0.0 ops = target.operation_names_for_qargs(qargs) for op in ops: props = target[op].get(qargs, None) if props is not None and props.error is not None: # Use max error rate to represent operation error # on a qubit(s). If there is more than 1 operation available # we don't know what will be used on the qubits eventually # so we take the highest error operation as a proxy for # the possible worst case. error = max(error, props.error) max_error = error if any(qubit not in qubit_map for qubit in qargs): continue # TODO: Factor in T1 and T2 to error matrix after #7736 if len(qargs) == 1: qubit = qubit_map[qargs[0]] error_mat[qubit][qubit] = max_error use_error = True elif len(qargs) == 2: error_mat[qubit_map[qargs[0]]][qubit_map[qargs[1]]] = max_error use_error = True elif backend_prop and coupling_map: error_dict = { tuple(gate.qubits): gate.parameters[0].value for gate in backend_prop.gates if len(gate.qubits) == 2 } for edge in coupling_map.get_edges(): gate_error = error_dict.get(edge) if gate_error is not None: if edge[0] not in qubit_map or edge[1] not in qubit_map: continue error_mat[qubit_map[edge[0]]][qubit_map[edge[1]]] = gate_error use_error = True for index, qubit_data in enumerate(backend_prop.qubits): if index not in qubit_map: continue for item in qubit_data: if == "readout_error": mapped_index = qubit_map[index] error_mat[mapped_index][mapped_index] = item.value use_error = True return error_mat, use_error