AI 中文总结
本文针对旅行商问题，比较研究暴力枚举、最小生成树2近似算法、模拟退火及量子近似优化算法这四种方法，通过实现并在不同规模图上评估来分析性能等，还开发开源框架供相关人员探索扩展这些方法。
AI 中文摘要
旅行商问题是经典的NP难问题，在物流、电路设计和运筹学中有重要意义。本文对解决旅行商问题的四种方法进行比较研究：暴力枚举、基于最小生成树的2近似算法、模拟退火和量子近似优化算法。实现各技术并在不同规模图上评估，分析性能、解质量和可扩展性，还开发了开源框架供研究人员和从业者探索、测试及扩展这些方法。
英文摘要
The Traveling Salesman Problem is a classical NP-hard problem with significant implications in logistics, circuit design, and operations research. This paper presents a comparative study of four approaches to solving the Traveling Salesman Problem: brute-force enumeration, a 2-approximation algorithm using minimum spanning trees, simulated annealing, and the Quantum Approximate Optimization Algorithm. We implement each technique and evaluate them on graphs of varying sizes to analyze performance, solution quality, and scalability. In doing so, we have also developed an open-source framework that allows researchers and practitioners to explore, test and extend these methods.
CommentsPresented at PEARC'26: "Practice and Experience in Advanced Research Computing", July 26-30, 2026. Minneapolis, MN, USA
Journal refACM PEARC'26 Proceedings (2026)
