# Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks

arXiv: https://arxiv.org/abs/2606.25480 · HTML: https://mlsanity.com/paper/2606.25480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2302.05007/) (2023)
- [Path Planning for Cellular-Connected UAV: A DRL Solution with Quantum-Inspired Experience Replay](https://mlsanity.com/paper/2108.13184/) (2021)
- [Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network](https://mlsanity.com/paper/2405.01314/) (2024)
- [Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks](https://mlsanity.com/paper/2502.17916/) (2025)
- [Quantum-Driven State-Reduction for Reliable UAV Trajectory Optimization in Low-Altitude Networks](https://mlsanity.com/paper/2510.17861/) (2025)
- [DQN Based Joint UAV Trajectory and Association Planning in NTN Assisted Networks](https://mlsanity.com/paper/2603.22127/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicle (UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation. However, it faces a challenge of curse of dimensionality in trajectory optimization, where interference-limited environments and vast search spaces make real-time coordination computationally expensive. To overcome this challenge, we propose the Rate-Aware Quantum-Annealed Graph Condensation (RA-QAGC) scheme, which combines rate-aware graph abstraction with decentralized reinforcement learning to enable scalable, interference-aware UAV coordination. By identifying high throughput locations and guiding UAV trajectory adaptation toward throughput-optimal regions, RA-QAGC effectively balances network capacity by maintaining quality-of-service (QoS) requirements. Simulation results demonstrate the proposal outperformed over existing schemes by achieving 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, representing gains of approximately 15% and 34%, respectively, over the baseline schemes.