Quantum‐Driven Optimization in UAV‐Assisted RIS Networks: A Comprehensive Framework for Maximizing Quantum‐Enhanced Signal Reconfiguration Efficiency

ABSTRACT This research presents a novel quantum‐assisted optimization framework for unmanned aerial vehicle‐assisted reconfigurable intelligent surface networks. It introduces a breakthrough metric known as quantum signal reconfiguration dynamics. Utilizing the principles of quantum computing, this framework enables real‐time signal propagation, optimizes resource usage, and reduces energy consumption in dynamic and complex scenarios. The integration of quantum algorithms significantly improves signal reconfiguration by allowing the resource allocator to coordinate control over reconfigurable intelligent surface elements effectively and make real‐time adjustments. This leads to enhanced signal strength, reduced interference, and lower overall energy consumption, minimizing the power requirements of the system. The framework employs optimization methods based on quantum computing, including the quantum approximate optimization algorithm and the variational quantum eigensolver, which can be adapted to the current network conditions. Stress‐testing using a computationally optimized fixed‐point algorithm demonstrates a threefold increase in signal reconfiguration dynamics, a fortyfold reduction in computational latency, and a 25% increase in coverage area compared to classical methods. These results represent significant progress in integrating reconfigurable intelligent surfaces with unmanned aerial vehicle‐assisted wireless communication networks. The proposed framework establishes a foundation for introducing much‐needed improvements to future communication architectures, particularly for sixth‐generation networks and beyond, promoting the development of intelligent and efficient communication systems.