Quantum annealing for clustering

Kenichi Kurihara; Shu Tanaka; Seiji Miyashita

Quantum annealing for clustering

Kenichi Kurihara^*, Shu Tanaka, Seiji Miyashita

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

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
Title of host publication	Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Pages	321-328
Number of pages	8
Publication status	Published - 2009
Externally published	Yes
Event	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada Duration: 2009 Jun 18 → 2009 Jun 21

Other

Other	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.",

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

year = "2009",

language = "English",

pages = "321--328",

booktitle = "Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009",

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

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AU - Kurihara, Kenichi

AU - Tanaka, Shu

AU - Miyashita, Seiji

PY - 2009

Y1 - 2009

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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M3 - Conference contribution

AN - SCOPUS:80053142047

SP - 321

EP - 328

BT - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

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

Y2 - 18 June 2009 through 21 June 2009

ER -

Quantum annealing for clustering

Abstract

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ASJC Scopus subject areas

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