Computational Performance of QAOA to Optimize Mesh Networks for Disaster Preparedness

Abstract

Positioning a radio mesh network across a mountainous region before a disaster strikes poses a difficult optimization problem: the terrain's topology obstructs radio signals, constraining which node placements can actually communicate. This work develops an optimization model for the placement problem and applies the Quantum Approximate Optimization Algorithm (QAOA) as a solution approach, evaluating its computational performance under different objective functions and warm-starting strategies across realistic terrain and equipment parameter settings, with experiments on both quantum hardware and classical simulations.

Presented in the Applied Quantum Optimization session at the 24th Conference of the International Federation of Operational Research Societies (IFORS 2026), Vienna, Austria.