As multi-drone fleets scale, zone assignment rapidly evolves into an intractable NP-hard combinatorial problem that overwhelms classical exhaustive search. While quantum optimization promises to shatter these classical bottlenecks, mapping complex spatial tasks from human intent to restricted quantum hardware remains a severe challenge. To bridge this gap, we present an end-to-end framework integrating a fine-tuned Large Language Model (LLM) front-end with a highly scalable, domain-specific quantum-classical backend. The front-end utilizes Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to translate free-form natural language instructions into structurally robust Quadratic Unconstrained Binary Optimization (QUBO) constraints without false negatives. To overcome the strict qubit limits of near-term quantum devices, our framework features a novel constraint-preserving graph partitioner and a compressed separator-based dynamic programming (DP) merge. By structurally encoding constraints via W-state initialization and XY-mixers in Conditional Value-at-Risk Quantum Approximate Optimization (CVaR-QAOA), the pipeline stays highly compact. Empirical results demonstrate that this architecture circumvents classical scaling walls, recovering the global optimum on 100% of idealized oracle cases and 96.3% under real QAOA sampling, enabling natural-language-guided task allocation at previously intractable scales.

翻译：随着多无人机编队规模的扩大，区域分配迅速演变为一个难以处理的NP难组合优化问题，令经典穷举搜索方法望而却步。尽管量子优化有望突破这些经典瓶颈，但将人类意图中的复杂空间任务映射到受限的量子硬件上仍是一个严峻挑战。为弥合这一鸿沟，我们提出了一种端到端框架，该框架集成了经过微调的大语言模型前端与高度可扩展的、面向特定领域的量子-经典后端。前端利用监督微调和直接偏好优化技术，将自由形式的自然语言指令转化为在结构上稳健、且无假负例的二次无约束二元优化约束。为了克服近期量子器件严格的量子比特限制，我们的框架采用了一种新颖的约束保持图分割器以及基于压缩分离器的动态规划合并方法。通过W态初始化和XY混频器在条件风险价值量子近似优化中进行结构化的约束编码，整个流水线保持高度紧凑。实验结果表明，该架构能够规避经典扩展壁垒，在理想化Oracle测试案例中100%恢复全局最优解，并在实际QAOA采样下达到96.3%的成功率，从而在以往难以处理的规模上实现了自然语言引导的任务分配。