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.
- Pub Date:
- May 2009
- Condensed Matter - Disordered Systems and Neural Networks;
- Condensed Matter - Statistical Mechanics;
- Computer Science - Machine Learning;
- Quantum Physics
- 8 pages, 6 figures, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) accepted