Quantum annealing for clustering

Authors:
Kenichi Kurihara;Shu Tanaka;Seiji Miyashita
Affiliations:
Google, Tokyo, Japan;University of Tokyo, Chiba, Japan;University of Tokyo, Tokyo, Japan and CREST, Saitama, Japan
Venue:
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Year:
2009

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Abstract

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.