用强化学习玩量子井字棋，探索量子与AI结合的新路径
Reinforcement learning for Quantum Tiq-Taq-Toe
- 设计基于测量和走法历史的量子状态表示方法
- 首次实现强化学习在量子井字棋中的应用
- 适合对量子机器学习感兴趣的入门研究者
量子井字棋是量子计算与机器学习领域的知名基准测试平台。尽管其广受欢迎，但此前尚未有强化学习（RL）方法应用于该游戏。虽然已有研究涉及量子象棋，但其计算和分析复杂度远高于量子井字棋。因此，本文研究量子计算与强化学习在量子井字棋中的结合，可作为两领域融合的可访问测试平台。量子游戏难以经典方式表示，因其具有固有的部分可观测性及潜在的指数级状态复杂度。在量子井字棋中，状态通过测量（3×3的状态概率矩阵）和走法历史（9×9的纠缠关系矩阵）观测，使得策略设计复杂，因为每一步操作都可能使量子态坍缩。
原文摘要 · Abstract (English)
Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have been applied to Quantum Tiq-Taq-Toe. Although there has been some research on Quantum Chess this game is significantly more complex in terms of computation and analysis. Therefore, we study the combination of quantum computing and reinforcement learning in Quantum Tiq-Taq-Toe, which may serve as an accessible testbed for the integration of both fields. Quantum games are challenging to represent classically due to their inherent partial observability and the potential for exponential state complexity. In Quantum Tiq-Taq-Toe, states are observed through Measurement (a 3x3 matrix of state probabilities) and Move History (a 9x9 matrix of entanglement relations), making strategy complex as each move can collapse the quantum state.
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