Quantum Annealing for Clustering

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dc.creatorKurihara, Kenichi
dc.creatorTanaka, Shu
dc.creatorMiyashita, Seiji
dc.date2009-05-21
dc.date2009-05-28
dc.date.accessioned2026-07-07T13:18:27Z
dc.date.available2026-07-07T13:18:27Z
dc.descriptionThis 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.
dc.description8 pages, 6 figures, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) accepted
dc.identifierhttps://arxiv.org/abs/0905.3527
dc.identifierhttp://arxiv.org/abs/0905.3527
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/231433
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.subjectMachine Learning
dc.subjectQuantum Physics
dc.titleQuantum Annealing for Clustering
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