Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
Winner takes it all: Training performant rl populations for combinatorial optimization.Advances in Neural Information Processing Systems, 36:48485–48509
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The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.
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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.