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

arXiv: https://arxiv.org/abs/2510.17861 · HTML: https://mlsanity.com/paper/2510.17861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks](https://mlsanity.com/paper/2606.25480/) (2026)

## Abstract (arXiv)

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.