量子智能体通过自主探索，重新发现多个重要量子算法。
Quantum Agents for Algorithmic Discovery
- 用奖励驱动的强化学习训练量子智能体，无需已知解
- 成功复现量子傅里叶变换、格罗弗搜索等经典算法
- 适合对量子算法自动设计感兴趣的科研人员
我们引入了通过分段式、基于奖励的强化学习训练的量子智能体，使其能够自主重新发现若干重要的量子算法与协议。具体而言，这些智能体学会了：实现量子傅里叶变换的高效对数深度量子电路；格罗弗搜索算法；强随机翻转中的最优欺骗策略；以及CHSH等非局域游戏中的最优获胜策略。智能体在未预先接触已知最优解的情况下，直接通过交互学习获得这些成果。这展示了量子智能作为算法发现工具的潜力，为新型量子算法与协议的自动化设计开辟了道路。
原文摘要 · Abstract (English)
We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search algorithm; optimal cheating strategies for strong coin flipping; and optimal winning strategies for the CHSH and other nonlocal games. The agents achieve these results directly through interaction, without prior access to known optimal solutions. This demonstrates the potential of quantum intelligence as a tool for algorithmic discovery, opening the way for the automated design of novel quantum algorithms and protocols.
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