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.

翻译：本文提出了一种利用量子计算能力解决现实世界无人机路径规划问题的新型混合方法。所提出的方法被命名为量子无人机路径规划（Q4DR），它整合了该领域两个最主流的范式：通过Eclipse Qrisp编程语言实现的量子门电路计算，以及借助D-Wave系统设备实现的量子退火器。该算法分为两个不同的阶段：使用量子近似优化算法（QAOA）执行的初始聚类阶段，以及采用量子退火器的路径规划阶段。Q4DR的有效性通过三个复杂度递增的用例得到验证，每个用例都包含了现实世界的约束条件，如非对称成本、禁止路径和移动充电点。这项研究为不断增长的量子优化领域贡献了力量，展示了量子计算在物流和路径规划中的实际应用前景。