Quantum Machine Learning for Performance Optimization of RIS-Assisted Communications: Framework Design and Application to Energy Efficiency Maximization of Systems with RSMA

This study proposes the utilization of quantum machine learning (QML) to maximize the energy efficiency of reconfigurable intelligent surface (RIS) assisted communication with rate-splitting multiple access (RSMA). The next-generation wireless communications are expected to yield significantly higher energy efficiency compared to that of the previous generations. In a multiuser system, energy efficiency can be defined as a benefit-to-cost ratio between the achievable sum-rate and the energy consumption, where enhancements in the former come at the expense of increases in the latter. Recently, the integration between RSMA and RISs has been advocated as a powerful mean to control this tradeoff. Indeed, RSMA can enhance the rate region while RISs can lead to reduced energy consumption thanks to the use of low-energy phase shifters. However, optimizing a RIS-aided RSMA communication system is faced with a computational burden given that the RIS enlarges the volume of the required channel information, which expands the information that needs to be processed by the optimization module, even when the optimization is based on conventional learning techniques. The proposed QML optimization framework, which orchestrates non-linear quantum unitary operations to compose the learning models, enjoys information processing gains thanks to state vector operations in multi-dimensional Hilbert space. It is composed of two trainable quantum-based learning models employed in an alternating manner: the first establishes the transmission precoding, and the second designs the RIS phase shifting. Numerical results show that the proposed QML delivers comparable performance to that of conventional optimization but with reduced complexity.