bloqade.lanes.heuristics.physical.nohome.NoHomePlacementStrategy

← Module overview

classNoHomePlacementStrategy¶source

bloqade.lanes.heuristics.physical.nohome.NoHomePlacementStrategy

Bases: NoReturnStrategyBase

Two-phase placement: return assignment + entangling routing.

Signature

class NoHomePlacementStrategy(arch_spec: ArchSpec, strategy: SearchStrategy = (lambda: SearchStrategy.IDS)(), max_expansions: int | None = 100, restarts: int = 20, deadlock_policy: DeadlockPolicy = (lambda: DeadlockPolicy.MOVE_BLOCKERS)(), top_c: int | None = None, backwards_search: bool = False, gamma: float = 0.85, lambda_lookahead: float = 0.5, k_candidates: int = 8, top_bus_signatures: int = 6, bus_reward_rho: int = 1)

Public import paths

• from bloqade.lanes.heuristics.physical import NoHomePlacementStrategy

Parameters

arch_spec: Architecture specification. strategy: Inner search strategy as a SearchStrategy enum used for the two routing phases. Default SearchStrategy.IDS. max_expansions: Maximum node expansions per solve call (shared across phases). restarts: Number of parallel restarts with perturbed scoring inside each routing solve (return phase + entangling phase). The two-phase Hungarian assignments themselves are deterministic; diversity comes from the candidate-home-layout fan-out. Default 20. deadlock_policy: DeadlockPolicy enum value controlling how the heuristic generator handles deadlocks during routing. Default DeadlockPolicy.MOVE_BLOCKERS. gamma: Discount factor for future CZ layer weights in the return assignment (default 0.85). lambda_lookahead: Blend weight for future proximity penalty in the return assignment (default 0.5). k_candidates: Maximum candidate holes per returner for cost-matrix pruning (default 8). top_bus_signatures: Number of bus-reward variant assignments to generate (default 6). Each variant rewards edges sharing a high-coverage lane signature, biasing the assignment toward layouts with parallel routing. bus_reward_rho: Per-edge hop-count discount applied to edges using a top signature when building bus-reward variant cost matrices (default 1).

Notes

Unlike NoReturnPlacementStrategy and RecedingHorizonNoReturnPlacementStrategy, this strategy does not pass top_c to SolveOptions (it inherits the base default None), matching the historical behaviour of the two-phase solve_nohome entry point.

Parameters

Name	Type	Default	Description
arch_spec	ArchSpec	required
strategy	SearchStrategy	(lambda: SearchStrategy.IDS)()
max_expansions	int | None	100
restarts	int	20
deadlock_policy	DeadlockPolicy	(lambda: DeadlockPolicy.MOVE_BLOCKERS)()
top_c	int | None	None
backwards_search	bool	False
gamma	float	0.85
lambda_lookahead	float	0.5
k_candidates	int	8
top_bus_signatures	int	6
bus_reward_rho	int	1

Attributes

Name	Type	Default	Description
restarts	int	20
gamma	float	0.85
lambda_lookahead	float	0.5
k_candidates	int	8
top_bus_signatures	int	6
bus_reward_rho	int	1