量子决策变压器（QDT）：协同纠缠与干涉用于离线强化学习
Quantum Decision Transformers (QDT): Synergistic Entanglement and Interference for Offline Reinforcement Learning
AI总结：
量子决策变压器通过结合量子启发的注意力和前馈网络，显著提升了离线强化学习中长周期信用分配和复杂状态-动作依赖性的处理能力。
摘要：
离线强化学习能够从预收集的数据集中学习策略，而无需与环境交互，但现有的决策变压器（DT）架构在长周期信用分配和复杂的状态-动作依赖性方面存在困难。我们引入了量子决策变压器（QDT），一种新的架构，结合了受量子启发的计算机制来解决这些挑战。我们的方法整合了两个核心组件：具有纠缠操作的量子启发注意力，能够捕捉非局部特征相关性，以及具有多路径处理和可学习干涉的量子前馈网络。通过在连续控制任务上的全面实验，我们展示了与标准DT相比的性能提升超过2000%，并且在不同数据质量下具有更好的泛化能力。关键的是，我们的消融研究揭示了量子启发组件之间的强协同效应：单独使用无法达到竞争性表现，但它们的结合产生了远超个体贡献的显著改进。这种协同表明，有效的量子启发架构设计需要整体设计相互依赖的机制，而不是模块化组件的采用。我们的分析确定了三个关键的计算优势：通过非局部相关性增强信用分配，通过并行处理实现隐式集束行为，以及通过可学习干涉实现自适应资源分配。这些发现确立了量子启发设计原则作为推进变压器架构在序列决策中的发展的重要方向，其影响不仅限于强化学习，还扩展到更广泛的神经网络架构设计。
英文摘要：
Offline reinforcement learning enables policy learning from pre-collected datasets without environment interaction, but existing Decision Transformer (DT) architectures struggle with long-horizon credit assignment and complex state-action dependencies. We introduce the Quantum Decision Transformer (QDT), a novel architecture incorporating quantum-inspired computational mechanisms to address these challenges. Our approach integrates two core components: Quantum-Inspired Attention with entanglement operations that capture non-local feature correlations, and Quantum Feedforward Networks with multi-path processing and learnable interference for adaptive computation. Through comprehensive experiments on continuous control tasks, we demonstrate over 2,000\% performance improvement compared to standard DTs, with superior generalization across varying data qualities. Critically, our ablation studies reveal strong synergistic effects between quantum-inspired components: neither alone achieves competitive performance, yet their combination produces dramatic improvements far exceeding individual contributions. This synergy demonstrates that effective quantum-inspired architecture design requires holistic co-design of interdependent mechanisms rather than modular component adoption. Our analysis identifies three key computational advantages: enhanced credit assignment through non-local correlations, implicit ensemble behavior via parallel processing, and adaptive resource allocation through learnable interference. These findings establish quantum-inspired design principles as a promising direction for advancing transformer architectures in sequential decision-making, with implications extending beyond reinforcement learning to neural architecture design more broadly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
