The field of wireless communications is witnessing a significant shift towards the adoption of reconfigurable intelligent surfaces (RIS) to enhance coverage, quality, and security. Recent research has focused on developing innovative architectures, optimization algorithms, and machine learning techniques to unlock the full potential of RIS technology. Notably, quasi-static IRS designs and continuous aperture array-based systems are being explored to achieve improved area coverage and energy efficiency. Furthermore, RIS-assisted over-the-air computation and neural network implementations are being investigated to enable fast and low-latency processing.

Some noteworthy papers in this area include:

• Quasi-Static IRS: 3D Shaped Beamforming for Area Coverage Enhancement, which introduces a novel architecture for quasi-static IRS and an alternating optimization algorithm for shaped beamforming.
• Realizing Fully-Connected Layers Over the Air via Reconfigurable Intelligent Surfaces, which proposes a new approach to implement neural networks using RIS-assisted multiple-input multiple-output systems.
• Adaptive Passive Beamforming in RIS-Aided Communications With Q-Learning, which presents a Q-learning-based strategy for adaptive RIS configuration without channel state information.
• Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems, which proposes a hybrid quantum-classical detection framework for RIS-aided broadband wireless communications.