用强化学习与搜索结合，让量子电路路由效率提升20%。
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search
- 用强化学习+蒙特卡洛树搜索自动优化量子比特路由路径。
- 相比现有方法，路由开销最多降低20%，提升程序效率。
- 适合量子算法开发与硬件资源紧张场景的开发者使用。
量子计算机在优化和数论分解等任务上具有超越经典计算机的潜力，但受限于连接性，需在程序执行过程中将量子比特（qubits）调度至特定位置以完成量子操作。传统方法依赖启发式规则，存在人为偏见且无法达到最优解。本文提出一种融合蒙特卡洛树搜索（MCTS）与强化学习（RL）的新型路由方案——AlphaRouter。该方法显著优于当前最先进的路由技术，在多个基准测试中实现最高达20%的路由开销降低，大幅提升了量子计算的整体效率与可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。