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

2026-01-27

AI Summary

This work addresses the challenge of maintaining reliable connectivity in dynamic unmanned aerial vehicle (UAV) networks, where rapidly changing topologies, fluctuating link quality, and stringent latency constraints render traditional global optimization methods computationally prohibitive and poorly adaptive. To overcome these limitations, the authors propose a two-stage quantum-assisted framework: in the offline phase, high-diversity, high-quality topology candidates are generated in parallel via quantum annealing based on a QUBO formulation; in the online phase, a lightweight classical mechanism selects the optimal topology in real time, balancing efficiency and robustness. This study presents the first application of quantum annealing to UAV network topology optimization, demonstrating a 6.6% improvement in performance retention over static optimal topologies within 30-second dynamic windows, a 5.15% enhancement in objective function value over classical approaches, and a 28.3% increase in solution diversity.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• UAV networks
• topology optimization
• dynamic environments
• resilient connectivity
• real-time adaptation

Innovation

• quantum annealing
• topology optimization
• UAV networks
• QUBO
• resilient control