Next-generation Unmanned Aerial Vehicle (UAV) communication networks must maintain reliable connectivity under rapid topology changes, fluctuating link quality, and time-critical data exchange. Existing topology control methods rely on global optimization to produce a single optimal topology or involve high computational complexity, which limits adaptability in dynamic environments. This paper presents a two-stage quantum-assisted framework for efficient and resilient topology control in dynamic UAV networks by exploiting quantum parallelism to generate a set of high-quality and structurally diverse candidate topologies. In the offline stage, we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model and leverage quantum annealing (QA) to parallelly sample multiple high-quality and structurally distinct topologies, providing a rich solution space for adaptive decision-making. In the online stage, a lightweight classical selection mechanism rapidly identifies the most suitable topology based on real-time link stability and channel conditions, substantially reducing the computation delay. The simulation results show that, compared to a single static optimal topology, the proposed framework improves performance retention by 6.6% in a 30-second dynamic window. Moreover, relative to the classic method, QA achieves an additional 5.15% reduction in objective value and a 28.3% increase in solution diversity. These findings demonstrate the potential of QA to enable fast and robust topology control for next-generation UAV communication networks.

翻译：下一代无人机通信网络必须在快速拓扑变化、链路质量波动和时间关键型数据交换下保持可靠的连接性。现有的拓扑控制方法依赖于全局优化以产生单一最优拓扑，或涉及较高的计算复杂度，这限制了其在动态环境中的适应性。本文提出了一种两阶段量子辅助框架，用于在动态无人机网络中实现高效且弹性的拓扑控制，该框架通过利用量子并行性来生成一组高质量且结构多样化的候选拓扑。在离线阶段，我们将该问题表述为一个二次无约束二进制优化模型，并利用量子退火并行采样多个高质量且结构各异的拓扑，为自适应决策提供丰富的解空间。在线阶段，一个轻量级的经典选择机制根据实时链路稳定性和信道条件快速识别最合适的拓扑，从而显著减少计算延迟。仿真结果表明，与单一静态最优拓扑相比，所提框架在30秒的动态窗口内将性能保持率提高了6.6%。此外，相较于经典方法，量子退火在目标值上额外降低了5.15%，并将解多样性提高了28.3%。这些发现证明了量子退火在实现下一代无人机通信网络快速、鲁棒拓扑控制方面的潜力。