Quantum Machine Learning for Wireless-Powered UAV Positioning in 6G Digital Twin SAGIN With Cooperative Nano-Satellite Constellations

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Abstract
Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

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