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

Researchers propose RA-QAGC, a quantum-inspired algorithm combining graph condensation with reinforcement learning to optimize UAV trajectory coordination in interference-limited networks. The approach demonstrates 15% throughput gains and 34% improvements in priority-user performance compared to existing methods, addressing scalability challenges in real-time multi-UAV coordination.

This research tackles a fundamental infrastructure challenge in autonomous aerial networks: coordinating multiple UAVs in interference-prone environments without prohibitive computational overhead. Traditional trajectory optimization becomes exponentially complex as the number of aircraft increases, creating a practical bottleneck for emergency response and connectivity applications. The RA-QAGC scheme bridges quantum computing concepts with classical reinforcement learning, using rate-aware graph abstraction to identify high-throughput zones and guide decentralized UAV coordination.

The broader context reflects growing investment in UAV infrastructure for disaster recovery and rural connectivity. Government agencies and telecom companies increasingly view aerial networks as critical infrastructure, particularly after natural disasters where ground systems fail. Academic progress in decentralized coordination directly enables commercial deployment at scale, reducing latency and computational requirements for real-time fleet management.

The performance metrics demonstrate practical applicability: 59.4 Mbps total throughput with 23.9 Mbps prioritized for critical users represents meaningful capacity for emergency communications. These gains emerge from smarter resource allocation rather than hardware improvements, suggesting the approach scales efficiently to larger networks. The decentralized learning architecture distributes computation across UAVs, eliminating central bottlenecks.

Future developments will likely focus on integration with 5G/6G infrastructure, real-world field testing, and scaling to 50+ aircraft coordination. The quantum-inspired elements may evolve into hardware-accelerated solutions as quantum processors mature. Organizations operating emergency response networks or developing autonomous fleet technologies should monitor this research trajectory, as breakthroughs could significantly reduce deployment costs and improve service reliability during critical incidents.

• →RA-QAGC combines quantum-annealing concepts with reinforcement learning to solve UAV trajectory optimization in interference-limited environments
• →The approach achieves 15% throughput gains and 34% priority-user throughput improvements over baseline schemes through rate-aware graph abstraction
• →Decentralized learning architecture eliminates computational bottlenecks by distributing coordination across individual UAV agents
• →The method maintains quality-of-service requirements while balancing network capacity across multiple concurrent users
• →Results suggest the approach scales effectively to larger UAV networks, supporting real-time emergency response and connectivity applications