一个参数独立的算法,用Seidel连续时间量子步行来找到最大的集群.
Xi Li1,2, Xiao Chen1,2, Shouwei Hu1,2
1School of Computer Science and Engineering, Southeast University, No.2, Sipailou District, Nanjing, Jiangsu 210096, China.
iScience
|February 9, 2024
概括
本研究引入了一种新的参数独立算法,用于在网络分析中找到最大集群 (MC). 使用Seidel矩阵的连续时间量子步行 (CTQW) 提供了稳定有效的MC识别.
科学领域:
- 量子计算是一种量子计算.
- 网络分析 网络分析
- 图形理论 图形理论
背景情况:
- 在网络分析中,最大集群 (MC) 问题至关重要.
- 目前的MC量子算法是依赖参数的,并且在各个图表中各不相同.
研究的目的:
- 开发一个稳定的,参数独立的算法来识别最大的集群.
- 探索由赛德尔矩阵驱动的连续时间量子步行 (CTQW) 对于MC检测的有效性.
主要方法:
- 在四种图形类型上深入分析CTQW,计算顶点幅度.
- 使用Seidel矩阵用于CTQW,优于相邻矩阵方法.
- 开发一种基于概率振幅强度的新型参数独立算法.
主要成果:
- 在最大点群中的顶点始终显示在概率幅度的最大频率组件的最高强度.
- 拟议的算法比典型的基于量子的算法具有更大的稳定性.
- 由Seidel矩阵驱动的CTQW提供了对图形集群结构的卓越洞察力.
结论:
- 赛德尔矩阵驱动的CTQW是理解图形集群结构的有希望的工具.
- 开发的参数独立算法为MC识别提供了稳定有效的方法.
- 这种方法推进了复杂网络分析问题的量子算法.
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