Quantum annealing for clustering

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita

Research output: Contribution to conferencePaperpeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Number of pages8
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada
Duration: 2009 Jun 182009 Jun 21


Conference25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
CityMontreal, QC

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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