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

TL;DR

This paper presents a quantum-inspired framework for reliable UAV trajectory optimization in low-altitude networks, reducing computational complexity while maintaining link quality and stability.

Contribution

It introduces a novel GC-QAP framework combining quantum-inspired graph condensation with reinforcement learning for UAV path planning.

Findings

Achieves stable convergence and low outage rates.

Reduces computational cost compared to baseline methods.

Maintains interference-aware link quality.

Abstract

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 epsilon-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.