用强化学习优化量子比特映射，减少约65%-85%的额外门开销。
CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
- 将量子比特映射建模为组合优化问题，用强化学习策略求解。
- 在真实电路数据集上实现65%-85%的SWAP门数量降低。
- 适合量子编译器研发者及关注硬件效率的研究人员。
量子编译器是量子计算流程中的关键环节，可将抽象量子电路转换为可在物理量子计算机上运行的形式。其中，逻辑比特到物理比特的映射是核心子问题。传统方法通常采用随机或启发式分配，以最小化额外的SWAP门开销。本文提出一种新方法：将比特映射问题建模为组合优化（CO）目标，并通过训练强化学习（RL）策略求解。此外，还设计了一种基于局部搜索的后处理算法进一步降低开销。实验表明，在真实世界数据集如MQTBench和Queko电路上，该方法相比现有编译器实现65%-85%的SWAP门开销减少。
原文摘要 · Abstract (English)
A quantum compiler is a critical piece in the quantum computing pipeline since it allows an abstract quantum circuit to be run on a physical quantum computer. One extremely important subproblem in quantum compilation is the generation of a logical to physical qubit mapping. Typically in quantum compilers this step is either implemented as a random or a heuristic based assignment that aims to minimize additional (SWAP) gate overhead in the quantum circuit. In this paper, we present an alternative approach to solving the qubit mapping problem. Specifically, we formulate the qubit mapping problem with a combinatorial optimization (CO) objective. We then present a method to find a solution to the CO problem by training a reinforcement learning (RL) policy. We also propose a local search based post-processing algorithm to further reduce the overhead. Our results show a dramatic improvement over conventional techniques in reducing the number of SWAPs. On different real world datasets like MQTBench and Queko circuits, our trained policy achieves a \textbf{65-85\%} reduction in SWAP overhead when compared to existing quantum compilers.
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