量子局部搜索结合路径切片策略求解旅行商问题
Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy
AI总结：
本文提出结合路径切片策略的量子局部搜索方法，通过k-means等聚类划分TSP子问题，混合量子经典求解，在TSPlib实例上高效获得近优解，提升求解效率与资源利用。
摘要：
我们提出了新颖的路径切片策略，并将其与量子局部搜索相结合，以优化旅行商问题（TSP）的解，从而解决当前含噪中等规模量子（NISQ）技术的局限性。我们的混合量子-经典方法利用经典路径初始化和量子优化，有效应对TSP带来的计算挑战。我们探索了多种路径切片方法，包括k-means和anti-k-means聚类，将TSP划分为可管理的子问题。这些子问题随后使用量子或经典求解器求解。我们对TSPlib中的多个TSP实例进行的分析表明，我们的策略能够高效地获得接近最优的解，突出了在求解效率和资源利用方面的显著改进。该方法为未来在更大规模组合优化场景中的应用铺平了道路，推动了量子优化领域的发展。
英文摘要：
We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current Noisy Intermediate-Scale Quantum (NISQ) technologies. Our hybrid quantum-classical approach leverages classical path initialization and quantum optimization to effectively manage the computational challenges posed by the TSP. We explore various path slicing methods, including k-means and anti-k-means clustering, to divide the TSP into manageable subproblems. These are then solved using quantum or classical solvers. Our analysis, performed on multiple TSP instances from the TSPlib, demonstrates the ability of our strategies to achieve near-optimal solutions efficiently, highlighting significant improvements in solving efficiency and resource utilization. This approach paves the way for future applications in larger combinatorial optimization scenarios, advancing the field of quantum optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
