Clustering quantum Markov chains on trees associated with open quantum random walks

被引：0

作者：

Accardi, Luigi ^{[1
]}

Andolsi, Amenallah ^{[2
]}

Mukhamedov, Farrukh ^{[3
]}

Rhaima, Mohamed ^{[4
]}

Souissi, Abdessatar ^{[5
]}

机构：

[1] Univ Roma Tor Vergata, Ctr Vito Volterra, I-00133 Rome, Italy

[2] Univ Tunis El Manar, Fac Sci Tunis, Nucl Phys & High Energy Phys Res Unit, Tunis 2092, Tunisia

[3] United Arab Emirates Univ, Coll Sci, Dept Math Sci, Al Ain 15551, U Arab Emirates

[4] King Saud Univ, Coll Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia

[5] Univ Carthage, Math Phys Quantum Modeling & Mech Design, Carthage 1054, Tunisia

来源：

AIMS MATHEMATICS | 2023年 / 8卷 / 10期

关键词：

Markov chains; quantum theory; clustering; Cayley tree; random walks; STATES; MODELS;

D O I：

10.3934/math.20231170

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In networks, the Markov clustering (MCL) algorithm is one of the most efficient approaches in detecting clustered structures. The MCL algorithm takes as input a stochastic matrix, which depends on the adjacency matrix of the graph network under consideration. Quantum clustering algorithms are proven to be superefficient over the classical ones. Motivated by the idea of a potential clustering algorithm based on quantum Markov chains, we prove a clustering property for quantum Markov chains (QMCs) on Cayley trees associated with open quantum random walks (OQRW).

引用

页码：23003 / 23015

页数：13

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