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

Luigi Accardi, Amenallah Andolsi, Farrukh Mukhamedov, Mohamed Rhaima, Abdessatar Souissi

Research output: Contribution to journalArticlepeer-review

Abstract

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).

Original languageEnglish
Pages (from-to)23003-23015
Number of pages13
JournalAIMS Mathematics
Volume8
Issue number10
DOIs
Publication statusPublished - 2023

Keywords

  • Markov chains
  • clustering, Cayley tree
  • quantum theory
  • random walks

ASJC Scopus subject areas

  • General Mathematics

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