Quantum annealing for clustering

Kenichi Kurihara; Shu Tanaka; Seiji Miyashita

Quantum annealing for clustering

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	321-328
Number of pages	8
Publication status	Published - 2009 Dec 1
Externally published	Yes
Event	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada Duration: 2009 Jun 18 → 2009 Jun 21

Conference

Conference	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Country/Territory	Canada
City	Montreal, QC
Period	09/6/18 → 09/6/21

ASJC Scopus subject areas

Artificial Intelligence
Applied Mathematics

Cite this

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title = "Quantum annealing for clustering",

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.",

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year = "2009",

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AU - Tanaka, Shu

AU - Miyashita, Seiji

PY - 2009/12/1

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N2 - 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.

AB - 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.

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T2 - 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Y2 - 18 June 2009 through 21 June 2009

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Quantum annealing for clustering

Abstract

Conference

ASJC Scopus subject areas

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