用量子元学习动态优化智能反射面，提升无线信号适应性。
Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces
- 基于历史表现选择量子路径，动态调整反射面相位
- 相比传统方法，频谱效率提升18.7%，收敛速度加快35%
- 适合高移动性场景下的智能无线网络部署
可重构智能表面（RIS）通过调控信号反射来增强无线通信能力。传统RIS相位优化在动态环境中因干扰和用户移动而高度非凸且困难。本文提出一种分层多目标量子元学习算法，根据历史成功记录、能耗和当前数据速率，在特定量子路径间切换。候选的RIS控制方向被构造成量子神经网络层间的切换路径，以最小化推理开销，每层通过评分机制选取最优路径。该算法不简单存储过往成功配置并匹配最近解，而是学习如何选择并重组不同解决方案中的最佳部分以应对新场景。模型将高维RIS场景特征通过张量积压缩为量子态，并在量子路径选择中叠加，显著提升量子计算优势。实验表明，该方法在频谱效率、收敛速度和适应性方面均有显著提升。
原文摘要 · Abstract (English)
Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high-dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.
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