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.
@article{arxiv.1408.2035,
title = {Quantum Annealing for Clustering},
author = {Kenichi Kurihara and Shu Tanaka and Seiji Miyashita},
journal= {arXiv preprint arXiv:1408.2035},
year = {2014}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)