This study realizes secure near-field location division multiple access through employing both a variational quantumcircuit and a nature-inspired algorithm. With the escalating demands of the sixth generation (6G) and beyond, there is growing interest in using large numbers of antenna elements,making near-field communications more practical Seizing this opportunity, we leverage near-field communications for a distinct multiple access technique, termed location division multipleaccess (LDMA), which capitalizes on spatial orthogonality to distinguish users by both angle and distance. Nevertheless, relying on beamforming to direct signals to distinct users poses a clear physical-layer security risk: adversaries might eavesdrop on messages intended for legitimate users, prompting the need tooptimize beamforming to enhance security while adhering towireless systems’ constraints. To make matters worse, conventional approaches typically entail multiple matrix inversions, and the optimization problem is far from trivial to solve due toits non-convexity and NP-hardness. To this end, our solutionleverages variational quantum circuits (VQC), motivated bythe potential benefits offered by quantum computing. On topof that, we improve upon the existing VQC workflows by integrating a classical algorithm, particularly, the differential evolution algorithm, thereby reducing quantum computational resource demands while maintaining high exploration efficiency. We further investigate how different quantum circuit depths influence the balance between expressibility and convergence. Simulation results reveal that our proposed scheme consistently outperforms conventional benchmarks in terms of secrecyrates, while simultaneously satisfying quality-of-service (QoS)constraints and power allocation requirements.

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.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.