Quantum Annealing for Clustering
Kenichi Kurihara, Shu Tanaka, Seiji Miyashita · arXiv (Cornell University) · 2014
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.