用自然语言指挥千架无人机，量子算法精准分配任务
Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment
- 用大模型将人话转为量子可解的数学约束
- 在有限量子比特下仍能找最优解，真实采样成功率96.3%
- 适合需要大规模智能调度的无人机、物流场景
随着多无人机编队规模扩大，区域分配迅速演变为难以求解的NP难组合问题，传统穷举法失效。尽管量子优化有望突破此瓶颈，但将人类意图映射到受限量子硬件仍面临挑战。为此，我们提出端到端框架：前端采用微调大语言模型（LLM），通过监督微调（SFT）和直接偏好优化（DPO）将自由文本指令转化为结构稳健的二次无约束二值优化（QUBO）约束，避免漏判；后端设计一种保持约束的图分割器与压缩型分离器动态规划合并策略，克服近中期量子设备的量子比特限制。通过W态初始化与XY混频器在条件风险量子近似优化（CVaR-QAOA）中结构化编码约束，整体流程高度紧凑。实证表明，该架构突破经典扩展瓶颈，在理想算例中100%恢复全局最优解，真实量子采样下达96.3%，实现此前不可行的大规模自然语言引导任务分配。
原文摘要 · Abstract (English)
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.
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