Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917029139709952 |
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| author | Kaleem, Zeeshan Afaq, Muhammad Yuen, Chau Dobre, Octavia A. Cioffi, John M. |
| author_facet | Kaleem, Zeeshan Afaq, Muhammad Yuen, Chau Dobre, Octavia A. Cioffi, John M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks Kaleem, Zeeshan Afaq, Muhammad Yuen, Chau Dobre, Octavia A. Cioffi, John M. Systems and Control 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. |
| title | Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.17861 |