量子智能遇见BD-RIS赋能的AmBC：挑战、机遇与实用洞察
Quantum Intelligence Meets BD-RIS-Enabled AmBC: Challenges, Opportunities, and Practical Insights
AI总结：
本文探讨了BD-RIS在AmBC中的应用，分析了其挑战与机遇，并通过量子增强的机器学习模型提升波束成形性能。
摘要：
超越对角线可重构智能表面（BD-RIS）是一种最近提出的一种新型可重构智能表面（RIS），被视为波操控领域的革命性进展。不同于传统RIS中互不相连的元件排列，BD-RIS创建了成本效益高且简单的元件间连接，允许在入射波的幅度和相位配置上拥有更大的自由度。然而，实现BD-RIS所关联的优势存在诸多潜在挑战，促使研究社区积极研究该方向的前沿方案和算法。特别是，在特定环境条件下，BD-RIS的被动波束成形设计已成为该研究领域的重点。在本文中，我们系统地介绍了BD-RIS，阐述了其功能原理，包括建筑设计、有前途的优势和分类。随后，我们介绍了最近的进展，并识别了一系列挑战和机遇。此外，我们考虑了一个具体案例研究，其中使用四种不同的算法设计波束成形，并分析了它们在总速率和计算成本方面的性能。为了增强6G BD-RIS中的波束成形能力，我们分析了各种混合量子-经典机器学习（ML）模型，以提高波束预测性能，利用来自DeepSense 6G数据集的现实通信场景8。因此，我们得出了关于BD-RIS实际意义的有用见解。
英文摘要：
A beyond-diagonal reconfigurable intelligent surface (BD-RIS) is an innovative type of reconfigurable intelligent surface (RIS) that has recently been proposed and is considered a revolutionary advancement in wave manipulation. Unlike the mutually disconnected arrangement of elements in traditional RISs, BD-RIS creates cost-effective and simple inter-element connections, allowing for greater freedom in configuring the amplitude and phase of impinging waves. However, there are numerous underlying challenges in realizing the advantages associated with BD-RIS, prompting the research community to actively investigate cutting-edge schemes and algorithms in this direction. Particularly, the passive beamforming design for BD-RIS under specific environmental conditions has become a major focus in this research area. In this article, we provide a systematic introduction to BD-RIS, elaborating on its functional principles concerning architectural design, promising advantages, and classification. Subsequently, we present recent advances and identify a series of challenges and opportunities. Additionally, we consider a specific case study where beamforming is designed using four different algorithms, and we analyze their performance with respect to sum rate and computation cost. To augment the beamforming capabilities in 6G BD-RIS with quantum enhancement, we analyze various hybrid quantum-classical machine learning (ML) models to improve beam prediction performance, employing real-world communication Scenario 8 from the DeepSense 6G dataset. Consequently, we derive useful insights about the practical implications of BD-RIS.
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。
