# Quantum Intelligence Meets BD-RIS-Enabled AmBC: Challenges, Opportunities, and Practical Insights

arXiv: https://arxiv.org/abs/2512.23400 · HTML: https://mlsanity.com/paper/2512.23400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Beyond Diagonal Reconfigurable Intelligent Surfaces: From Transmitting and Reflecting Modes to Single-, Group-, and Fully-Connected Architectures](https://mlsanity.com/paper/2205.02866/) (2022)
- [Closed-Form Global Optimization of Beyond Diagonal Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2211.06117/) (2022)
- [Reconfigurable Intelligent Surfaces 2.0: Beyond Diagonal Phase Shift Matrices](https://mlsanity.com/paper/2301.03288/) (2023)
- [Beyond Diagonal Reconfigurable Intelligent Surfaces Utilizing Graph Theory: Modeling, Architecture Design, and Optimization](https://mlsanity.com/paper/2305.05013/) (2023)
- [Wideband Channel Capacity Maximization With Beyond Diagonal RIS Reflection Matrices](https://mlsanity.com/paper/2404.00982/) (2024)
- [A Low-Complexity Beamforming Design for Beyond-Diagonal RIS aided Multi-User Networks](https://mlsanity.com/paper/2307.09807/) (2023)
- [Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?](https://mlsanity.com/paper/2402.14157/) (2024)
- [Transmitter Side Beyond-Diagonal RIS for mmWave Integrated Sensing and Communications](https://mlsanity.com/paper/2404.12604/) (2024)
- [MIMO Capacity Maximization with Beyond-Diagonal RIS](https://mlsanity.com/paper/2406.02170/) (2024)
- [Cell-Free Massive MIMO SWIPT with Beyond Diagonal Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2507.23702/) (2025)

## Abstract (arXiv)

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.