用量子计算解决无人机路径规划难题，融合两种量子技术。
Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms
- 分两阶段：先用量子近似优化算法聚类，再用量子退火求解路径。
- 在真实场景中验证，处理了非对称成本、禁行路段等复杂约束。
- 适合关注量子优化与物流应用的科研人员和工程师。
本文提出一种新型混合方法——量子无人机路径规划（Q4DR），用于解决现实世界的无人机路径规划问题。该方法结合了量子门模型计算（通过Eclipse Qrisp编程语言）和量子退火器（基于D-Wave系统设备）两大主流范式。算法分为两个阶段：第一阶段使用量子近似优化算法（QAOA）进行初始聚类；第二阶段采用量子退火器求解具体路径。通过三个复杂度递增的真实案例验证了Q4DR的有效性，这些案例包含非对称成本、禁行路径及移动充电点等实际约束。研究展示了量子计算在物流与路径规划中的实际应用潜力，为量子优化领域提供了重要实践参考。
原文摘要 · Abstract (English)
This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.
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