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

TL;DR

This paper introduces a quantum-assisted two-stage framework for resilient UAV network topology control, leveraging quantum annealing to generate diverse high-quality topologies for improved adaptability and performance in dynamic environments.

Contribution

It presents a novel quantum-assisted approach that combines offline QUBO modeling with online classical selection for efficient, resilient UAV network topology management.

Findings

Performance retention improved by 6.6% in dynamic scenarios

Quantum annealing reduces objective value by 5.15%

Solution diversity increased by 28.3%

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.