Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A hybrid classical-quantum clustering algorithm based on quantum walks

A hybrid classical-quantum clustering algorithm based on quantum walks The enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum walk (QW) with the problem of data clustering, and develop two clustering algorithms based on the one-dimensional discrete-time QW. Then, the position probability distributions induced by QW in these algorithms are investigated, which also indicates the possibility of obtaining better results. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quantum Information Processing Springer Journals

A hybrid classical-quantum clustering algorithm based on quantum walks

Download PDF
14 pages

Loading next page...
1
 
/lp/springer_journal/a-hybrid-classical-quantum-clustering-algorithm-based-on-quantum-walks-GwHSufFBIH

References (26)

Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Physics; Quantum Information Technology, Spintronics; Quantum Computing; Data Structures, Cryptology and Information Theory; Quantum Physics; Mathematical Physics
ISSN
1570-0755
eISSN
1573-1332
DOI
10.1007/s11128-010-0169-y
Publisher site
See Article on Publisher Site

Abstract

The enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum walk (QW) with the problem of data clustering, and develop two clustering algorithms based on the one-dimensional discrete-time QW. Then, the position probability distributions induced by QW in these algorithms are investigated, which also indicates the possibility of obtaining better results. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

Journal

Quantum Information Processing – Springer Journals

Published: Mar 10, 2010

There are no references for this article.