Quantum Annealing for Clustering

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:321-328, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-kurihara09a, title = {Quantum Annealing for Clustering}, author = {Kurihara, Kenichi and Tanaka, Shu and Miyashita, Seiji}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {321--328}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/kurihara09a/kurihara09a.pdf}, url = {https://proceedings.mlr.press/r7/kurihara09a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Quantum Annealing for Clustering %A Kenichi Kurihara %A Shu Tanaka %A Seiji Miyashita %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-kurihara09a %I PMLR %P 321--328 %U https://proceedings.mlr.press/r7/kurihara09a.html %V R7 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Kurihara, K., Tanaka, S. & Miyashita, S.. (2009). Quantum Annealing for Clustering. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:321-328 Available from https://proceedings.mlr.press/r7/kurihara09a.html. Reissued by PMLR on 04 October 2026.

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