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 language | English |
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Pages (from-to) | 23003-23015 |
Number of pages | 13 |
Journal | AIMS Mathematics |
Volume | 8 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- Markov chains
- clustering, Cayley tree
- quantum theory
- random walks
ASJC Scopus subject areas
- General Mathematics