Quantum annealing for clustering

Kenichi Kurihara; Shu Tanaka; Seiji Miyashita

Quantum annealing for clustering

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita

研究成果: Paper › 査読

抄録

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.

本文言語	English
ページ	321-328
ページ数	8
出版ステータス	Published - 2009 12月 1
外部発表	はい
イベント	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada 継続期間: 2009 6月 18 → 2009 6月 21

Conference

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

ASJC Scopus subject areas

人工知能
応用数学

他のファイルとリンク

引用スタイル

@conference{c188385c187d48678a6088b096dfeb0d,

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.",

author = "Kenichi Kurihara and Shu Tanaka and Seiji Miyashita",

year = "2009",

month = dec,

day = "1",

language = "English",

pages = "321--328",

note = "25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 ; Conference date: 18-06-2009 Through 21-06-2009",

}

TY - CONF

T1 - Quantum annealing for clustering

AU - Kurihara, Kenichi

AU - Tanaka, Shu

AU - Miyashita, Seiji

PY - 2009/12/1

Y1 - 2009/12/1

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.

UR - http://www.scopus.com/inward/record.url?scp=80053142047&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053142047&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:80053142047

SP - 321

EP - 328

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

Y2 - 18 June 2009 through 21 June 2009

ER -

Quantum annealing for clustering

抄録

Conference

ASJC Scopus subject areas

他のファイルとリンク

フィンガープリント

引用スタイル