A Hybrid Classical-Quantum Computing Framework for RIS-assisted Wireless Network

Recently, there has been a growing interest in employing reconfigurable intelligent surfaces (RISs) to improve the spectrum and energy efficiency of wireless networks. In RIS-assisted wireless networks, channel estimation and optimization is a difficult task, particularly for nearly passive RIS devices with low-complexity hardware design. We have proposed a hybrid classical-quantum computing framework that allows for ultra-fast optimization adapting to multipath wireless environments. The onsite optimization of RIS configuration can be performed almost instantaneously using only feedback (received power) at wireless endpoints. The performance of the proposed work is demonstrated in representative wireless propagation scenarios.