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arXiv · 2510.17861

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

Zeeshan Kaleem·Muhammad Afaq·Chau Yuen·Octavia A. Dobre·John M. Cioffi
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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.

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Zeeshan Kaleem, Muhammad Afaq, Chau Yuen, Octavia A. Dobre, John M. Cioffi. 2025-10-15. Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks. https://arxiv.org/abs/2510.17861

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