Quantum Annealing for Clustering
  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. 
Kenichi Kurihara, Shu Tanaka, Seiji Miyashita
cs.AI cs.LG