混合量子-经典专家混合模型：通过基于干涉的路由解锁拓扑优势
Hybrid Quantum-Classical Mixture of Experts: Unlocking Topological Advantage via Interference-Based Routing
AI总结：
本文提出混合量子-经典专家模型，通过量子干涉机制提升非线性决策边界建模效率，实现在拓扑优势上的突破。
摘要：
混合专家（MoE）架构已逐渐成为扩展深度学习模型的强大范式，但其本质上受到专家不平衡和经典路由机制计算复杂性等挑战的限制。本文研究了量子机器学习（QML）通过一种新颖的混合量子-经典混合专家（QMoE）架构来解决这些限制的潜力。具体来说，我们使用量子门网络（路由器）结合经典专家进行消融研究，以隔离量子优势的来源。我们的核心发现验证了干涉假设：通过利用量子特征图（角度嵌入）和波干涉，量子路由器充当高维核方法，能够以优于其经典对应物的参数效率建模复杂、非线性决策边界。在非线性可分数据集（如Two Moons数据集）上的实验结果表明，量子路由器实现了显著的拓扑优势，有效“解开”线性经典路由器无法高效分离的数据分布。此外，我们分析了该架构对模拟量子噪声的鲁棒性，确认其在近中期中等规模量子（NISQ）硬件中的可行性。我们讨论了联邦学习、隐私保护机器学习和能从这种量子增强路由范式中获益的适应性系统等实际应用。
英文摘要：
The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of classical routing mechanisms. This paper investigates the potential of Quantum Machine Learning (QML) to address these limitations through a novel Hybrid Quantum-Classical Mixture of Experts (QMoE) architecture. Specifically, we conduct an ablation study using a Quantum Gating Network (Router) combined with classical experts to isolate the source of quantum advantage. Our central finding validates the Interference Hypothesis: by leveraging quantum feature maps (Angle Embedding) and wave interference, the Quantum Router acts as a high-dimensional kernel method, enabling the modeling of complex, non-linear decision boundaries with superior parameter efficiency compared to its classical counterparts. Experimental results on non-linearly separable data, such as the Two Moons dataset, demonstrate that the Quantum Router achieves a significant topological advantage, effectively "untangling" data distributions that linear classical routers fail to separate efficiently. Furthermore, we analyze the architecture's robustness against simulated quantum noise, confirming its feasibility for near-term intermediate-scale quantum (NISQ) hardware. We discuss practical applications in federated learning, privacy-preserving machine learning, and adaptive systems that could benefit from this quantum-enhanced routing paradigm.
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。
