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条件下保持可靠连接。现有拓扑控制方法依赖全局优化生成单一最优拓扑或涉及高计算复杂度，限制了在动态环境中的适应性。本文提出一种两阶段量子辅助框架，通过利用量子并行性生成一组高质量且结构多样的候选拓扑，实现动态无人机网络的高效弹性拓扑控制。在离线阶段，我们将问题建模为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，轻量级经典选择机制基于实时链路稳定性和信道条件快速识别最合适的拓扑，显著降低计算延迟。仿真结果表明，相较于单一静态最优拓扑，所提框架在30秒动态窗口内将性能保持率提升了6.6%。此外，相对于经典方法，量子退火使目标值额外降低5.15%，解多样性提高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。