量子纠缠让游戏代理在对抗中表现更优，实验证明其具学习优势。
Quantum entanglement provides a competitive advantage in adversarial games
- 用8量子比特电路做特征提取，对比有无纠缠的策略
- 含纠缠的模型胜率更高，低容量下甚至超过经典神经网络
- 适合对量子增强学习感兴趣的研究者或算法设计者
量子资源是否能在完全经典、竞争性的环境中带来优势仍是未解之谜。对抗性零和强化学习尤为困难，因其需建模对手之间的动态交互，而非静态状态-动作映射。本文通过控制实验，在Pong这一竞争性马尔可夫游戏中研究量子纠缠的作用。采用8量子比特参数化电路作为近端策略优化框架中的特征提取器，直接比较可分离电路与固定（CZ）或可训练（IsingZZ）纠缠门架构。结果表明，含纠缠的电路在参数量相近时持续优于可分离版本；在低容量情况下，性能可匹配甚至超越经典多层感知机基线。表示相似性分析显示，纠缠电路学习到结构不同的特征，支持其对交互状态变量的更好建模。这些发现确立了纠缠作为竞争性强化学习中表示学习的功能性资源。
原文摘要 · Abstract (English)
Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question. Competitive zero-sum reinforcement learning is particularly challenging, as success requires modelling dynamic interactions between opposing agents rather than static state-action mappings. Here, we conduct a controlled study isolating the role of quantum entanglement in a quantum-classical hybrid agent trained on Pong, a competitive Markov game. An 8-qubit parameterised quantum circuit serves as a feature extractor within a proximal policy optimisation framework, allowing direct comparison between separable circuits and architectures incorporating fixed (CZ) or trainable (IsingZZ) entangling gates. Entangled circuits consistently outperform separable counterparts with comparable parameter counts and, in low-capacity regimes, match or exceed classical multilayer perceptron baselines. Representation similarity analysis further shows that entangled circuits learn structurally distinct features, consistent with improved modelling of interacting state variables. These findings establish entanglement as a function resource for representation learning in competitive reinforcement learning.
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