用量子计算实现强化学习，全程无需经典计算。
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
- 将马尔可夫决策过程完全量子化，利用叠加态加速计算。
- 量子轨迹搜索显著提升决策效率，实现量子增强。
- 适合对量子机器学习感兴趣的科研人员和工程师。
本文提出一种基于量子原理的强化学习框架，将经典的马尔可夫决策过程（MDP）完全量子化，实现智能体与环境交互的全量子化建模。通过量子算术与量子搜索算法，构建了基于量子态的转移、回报计算与轨迹搜索机制，完整展示了强化学习过程在量子体系中的实现。该方法充分利用量子叠加态特性，在不依赖经典计算的前提下，显著提升了计算效率。实验表明，该量子模型在决策任务中展现出量子增强潜力，为量子强化学习（QRL）提供了坚实的方法论支持。本工作不仅拓展了量子计算在机器学习中的应用边界，也为未来量子智能系统的设计提供了新范式。
原文摘要 · Abstract (English)
This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov decision process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment interactions entirely within the quantum domain, eliminating reliance on classical computations. Key contributions include the quantum-based state transitions, return calculation, and trajectory search mechanism that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena. The implementation emphasizes the fundamental role of quantum superposition in enhancing computational efficiency for RL tasks. Results demonstrate the capacity of a quantum model to achieve quantum enhancement in RL, highlighting the potential of fully quantum implementations in decision-making tasks. This work not only underscores the applicability of quantum computing in machine learning but also contributes to the field of quantum reinforcement learning (QRL) by offering a robust framework for understanding and exploiting quantum computing in RL systems.
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