Uncertainty in Artificial Intelligence
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Quantum Annealing for Clustering
Kenichi Kurihara, Shu Tanaka, Seiji Miyashita
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.
Keywords: null
Pages: 321-328
PS Link:
PDF Link: /papers/09/p321-kurihara.pdf
AUTHOR = "Kenichi Kurihara and Shu Tanaka and Seiji Miyashita",
TITLE = "Quantum Annealing for Clustering",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "321--328"

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