Quantum computers have the potential to outperform classical computers in important tasks such as optimization and number factoring. They are characterized by limited connectivity, which necessitates the routing of their computational bits, known as qubits, to specific locations during program execution to carry out quantum operations. Traditionally, the NP-hard optimization problem of minimizing the routing overhead has been addressed through sub-optimal rule-based routing techniques with inherent human biases embedded within the cost function design. This paper introduces a solution that integrates Monte Carlo Tree Search (MCTS) with Reinforcement Learning (RL). Our RL-based router, called AlphaRouter, outperforms the current state-of-the-art routing methods and generates quantum programs with up to $20\%$ less routing overhead, thus significantly enhancing the overall efficiency and feasibility of quantum computing.

翻译：量子计算机在优化与因数分解等重要任务中展现出超越经典计算机的潜力。其架构受限于量子比特间的有限连通性，因此在程序执行过程中需将计算比特（即量子比特）路由至特定位置以执行量子操作。传统上，这一最小化布线开销的NP难优化问题通常通过基于规则的次优布线技术解决，其代价函数设计中往往存在固有的人工偏差。本文提出一种融合蒙特卡洛树搜索与强化学习的解决方案。我们基于强化学习的布线器（命名为AlphaRouter）在性能上超越了当前最先进的布线方法，生成的量子程序可降低高达$20\%$的布线开销，从而显著提升了量子计算的整体效率与可行性。