Beyond-Diagonal Reconfigurable Intelligent Surfaces for 6G Networks: Principles, Challenges, and Quantum Horizons

2025-12-29

AI Summary

In 6G integrated sensing and communication (ISAC) networks, conventional reconfigurable intelligent surfaces (RISs) suffer from diagonal-only beam control, limiting flexible joint amplitude-phase beamforming. Method: This paper proposes a Beyond-Diagonal RIS (BD-RIS) architecture enabling non-diagonal coupling among unit cells via low-cost interconnections. We establish the first theoretical framework and taxonomy for BD-RISs, develop a passive beamforming co-optimization method, and pioneer a quantum-classical hybrid machine learning approach—combining quantum neural networks (QNNs) and variational quantum circuits (VQCs)—trained on real-world DeepSense 6G measurement data. Results: Evaluated in Scenario 8, the quantum-enhanced model achieves a 23.7% improvement in beam prediction accuracy and a 41% reduction in latency. Moreover, BD-RIS delivers over 35% higher sum rate compared to conventional diagonal RISs.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Behavior Learning & Control

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Designing passive beamforming for BD-RIS under specific environmental conditions
• Addressing challenges in realizing BD-RIS advantages with advanced schemes and algorithms
• Enhancing 6G BD-RIS beamforming using hybrid quantum-classical machine learning models

Innovation

• BD-RIS enables inter-element connections for wave control
• Passive beamforming design optimized under specific conditions
• Hybrid quantum-classical ML models enhance beam prediction