Hu Xinqi, Dong Gaogao, Christensen Kim, Sun Hanlin, Fan Jingfang, Tian Zihao, Gao Jianxi, Havlin Shlomo, Lambiotte Renaud, Meng Xiangyi
School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China.
Sci Adv. 2025 Feb 28;11(9):eadt2404. doi: 10.1126/sciadv.adt2404. Epub 2025 Feb 26.
Quantum networks (QNs) exhibit stronger connectivity than predicted by classical percolation, yet the origin of this phenomenon remains unexplored. We apply a statistical physics model-concurrence percolation-to uncover the origin of stronger connectivity on hierarchical scale-free networks, the (, ) flowers. These networks allow full analytical control over path connectivity through two adjustable path-length parameters, ≤. This precise control enables us to determine critical exponents well beyond current simulation limits, revealing that classical and concurrence percolations, while both satisfying the hyperscaling relation, fall into distinct universality classes. This distinction arises from how they "superpose" parallel, nonshortest path contributions into overall connectivity. Concurrence percolation, unlike its classical counterpart, is sensitive to nonshortest paths and shows higher resilience to detours as these paths lengthen. This enhanced resilience is also observed in real-world hierarchical, scale-free internet networks. Our findings highlight a crucial principle for QN design: When nonshortest paths are abundant, they notably enhance QN connectivity beyond what is achievable with classical percolation.
量子网络(QNs)展现出比经典渗流理论预测更强的连通性,然而这一现象的起源仍未得到探索。我们应用一种统计物理模型——并发渗流,来揭示分层无标度网络((, )花型网络)上更强连通性的起源。这些网络通过两个可调节的路径长度参数≤,实现了对路径连通性的完全解析控制。这种精确控制使我们能够确定远超当前模拟极限的临界指数,结果表明经典渗流和并发渗流虽然都满足超标度关系,但属于不同的普适类。这种差异源于它们将平行的非最短路径贡献“叠加”到整体连通性的方式。与经典渗流不同,并发渗流对非最短路径敏感,并且随着这些路径变长,对迂回具有更高的弹性。在现实世界的分层无标度互联网网络中也观察到了这种增强的弹性。我们的研究结果突出了量子网络设计的一个关键原则:当非最短路径丰富时,它们能显著增强量子网络的连通性,远超经典渗流所能达到的水平。