Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks

This letter introduces a graph-condensed quantum-inspired placement (GC-QAP) framework for reliability-driven trajectory optimization in uncrewed aerial vehicle (UAV)-assisted low-altitude wireless networks. The dense waypoint graph is condensed using probabilistic quantum-annealing to preserve interference-aware centroids while reducing the control state space and maintaining link-quality. The resulting problem is formulated as a priority-aware Markov decision process and solved using ϵ-greedy off-policy Q-learning, considering UAV kinematic and flight corridor constraints. Unlike complex continuous-action reinforcement learning approaches, GC-QAP achieves stable convergence and low outage with substantially and lower computational cost compared to baseline schemes.