"""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 will 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

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.uint32,

count=len(qubit_subset),

)

int_gates = np.fromiter(

(dag.find_bit(bit).index for gate in gates for bit in gate),

dtype=np.uint32,

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.uint32).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), (), check=False)

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, layer, 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)

if self.property_set["final_layout"] is None:

self.property_set["final_layout"] = layout

else:

self.property_set["final_layout"] = layout.compose(

self.property_set["final_layout"], circuit_graph.qubits

)

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, check=False)

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,

"""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

"""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,

check=False,

)

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)