Quantum Takes Flight: Two-Stage Resilient Topology Optimization for UAV Networks

TL;DR

This work addresses the fragility of fixed UAV network topologies in dynamic environments by proposing a two-stage quantum-assisted topology control framework. It offline generates a diverse set of candidate topologies through a QUBO formulation solved by quantum annealing, and online selects the best topology using real-time SINR and residual energy, enabling fast adaptation. The key contributions include a QUBO model with a diversity-penalty, a frequency-based offline sampling approach, and a lightweight online evaluation that together improve dynamic performance and resilience while aligning with SDN/O-RAN architectures. The results demonstrate meaningful gains in stability and solution diversity, illustrating the practical potential of quantum-assisted topology management for next-generation UAV networks.

Abstract

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.

Figures (5)

• Figure 1: Two-stage topology control framework: offline generation of diverse candidate topologies via QA (Steps 1-2), and online lightweight selection based on real-time network conditions (Step 3).
• Figure 2: Comparison of offline objective value and solution diversity between QA and SA under five UAV application scenarios, I0 through I4.
• Figure 3: Runtime scaling of a classical solver and a quantum annealer for $N=25, 50, 100$ (log–log). Error bars show standard deviation across runs.
• Figure 4: Trade-off between network throughput and load balance as a function of the fragility weight $\beta$. Throughput (blue, left axis) decreases as $\beta$ increases, while load balance (red, right axis), measured by the standard deviation of nodal load, improves accordingly.
• Figure 5: Normalized throughput over 30 s under mobility with mission disturbances. The dashed line at $y=1$ marks the normalization reference, and shaded bands show 95% confidence intervals over 20 runs. The dynamic two-stage framework ($PR = 0.920$) outperforms the single optimization model ($PR = 0.863$) by 6.6%, showing faster recovery and greater stability.