SabreLayout

class qiskit.transpiler.passes.SabreLayout(*args, **kwargs)

Bases: 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 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.

In addition to starting with a random initial Layout the pass can also take in an additional list of starting layouts which will be used for additional trials. If the sabre_starting_layouts is present in the property set when this pass is run, that will be used for additional trials. There will still be layout_trials of full random starting layouts run and the contents of sabre_starting_layouts will be run in addition to those. The output which results in the lowest amount of swap gates (whether from the random trials or the property set starting point) will be used. The value for this property set field should be a list of Layout objects representing the starting layouts to use. If a virtual qubit is missing from an Layout object in the list a random qubit will be selected.

Property Set Fields Read

sabre_starting_layouts (list[Layout])

An optional list of Layout objects to use for additional layout trials. This is in addition to the full random trials specified with the layout_trials argument.

Property Set Values Written

layout (Layout)

The chosen initial mapping of virtual to physical qubits, including the ancilla allocation.

final_layout (Layout)

A permutation of how swaps have been applied to the input qubits at the end of the circuit.

References:

[1] Henry Zou and Matthew Treinish and Kevin Hartman and Alexander Ivrii and Jake Lishman. “LightSABRE: A Lightweight and Enhanced SABRE Algorithm” arXiv:2409.08368 [2] Li, Gushu, Yufei Ding, and Yuan Xie. “Tackling the qubit mapping problem for NISQ-era quantum devices.” ASPLOS 2019. arXiv:1809.02573

SabreLayout initializer.

param coupling_map

directed graph representing a coupling map.

type coupling_map

Union[CouplingMap, Target]

param routing_pass

the routing pass to use while iterating. If specified this pass operates as an 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.

type routing_pass

BasePass

param seed

seed for setting a random first trial layout.

type seed

int

param max_iterations

number of forward-backward iterations.

type max_iterations

int

param swap_trials

The number of trials to run of SabreSwap for each iteration. This is equivalent to the trials argument on 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.

type swap_trials

int

param layout_trials

The number of random seed trials to run layout with. When > 1 the trial that results 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.

type layout_trials

int

param skip_routing

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 set 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.

type skip_routing

bool

raises TranspilerError

If both routing_pass and swap_trials or

raises both routing_pass and layout_trials are specified

Attributes

is_analysis_pass

Check if the pass is an analysis pass.

If the pass is an AnalysisPass, that means that the pass can analyze the DAG and write the results of that analysis in the property set. Modifications on the DAG are not allowed by this kind of pass.

is_transformation_pass

Check if the pass is a transformation pass.

If the pass is a TransformationPass, that means that the pass can manipulate the DAG, but cannot modify the property set (but it can be read).

Methods

execute

execute(passmanager_ir, state, callback=None)

GitHub

Execute optimization task for input Qiskit IR.

Parameters

passmanager_ir (Any) – Qiskit IR to optimize.
state (PassManagerState) – State associated with workflow execution by the pass manager itself.
callback (Callable | None) – A callback function which is caller per execution of optimization task.