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原文摘要

Placing unmanned aerial vehicles (UAVs) as aerial base stations is non-convex and combinatorial, so practical methods first discretize the continuous airspace into a finite set of candidate positions---a uniform grid or clustering centroids---and optimize over that set. Built from Euclidean distance alone, the candidate set is channel-blind: it ignores line-of-sight probability and the co-channel interference that limits achievable rate under full frequency reuse. No optimizer can select a position the candidate set omits, so good locations the geometry missed are lost before the search begins. We propose Rate-Aware Quantum-Assisted Graph Condensation (RA-QAGC), a two-stage framework that makes the candidate set itself the object of optimization. The first stage constructs it by annealing a cost that embeds tier-weighted achievable rate, mean co-channel interference, and an explicit minimum-rate penalty, yielding a compact, mobility-feasible waypoint graph. The second stage learns trajectories over that graph with decentralized independent Q-learning agents, replacing a centralized joint-action table with one compact table per UAV. RA-QAGC improves priority-tier throughput by 21--54\% over seven baselines, including recent graph-neural and deep Q-network schemes, at no significant cost in aggregate throughput, and the margin widens as the fleet densifies. We additionally examine the quantum-inspired acceptance rule that recent aerial-networking work advocates, and report that its tunnelling floor confers no measurable advantage over classical simulated annealing at any fleet size tested, while annealing itself clearly outperforms greedy and random placement.