Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment

• URL: http://arxiv.org/abs/2606.14252v1
• Date: Fri, 12 Jun 2026 08:31:14 GMT
• Title: Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment
• Abstract summary: We present an end-to-end framework integrating a fine-tuned Large Language Model front-end with a highly scalable, domain-specific quantum-classical backend.<n>Our framework features a novel constraint-preserving graph partitioner and a compressed separator-based dynamic programming (DP) merge.<n> Empirical results demonstrate that this architecture circumvents classical scaling walls, recovering the global optimum on 100% of idealized cases and 96.3% under real QAOA sampling.
• Score: 1.189955933770711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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