Quantum Annealing for Clustering
arXiv:1408.2035v131 citations
Originality Incremental advance
AI Analysis
This is an incremental improvement for researchers in quantum computing and clustering, offering a potentially more effective method.
The paper tackled clustering by extending simulated annealing to quantum annealing, proposing an algorithm and schedule, and found that it yields better clustering assignments than simulated annealing in experiments.
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.