Quantum Annealing for Clustering

2009/05/21 by Kenichi Kurihara, Kurihara, Kenichi, Shu Tanaka +3 · 2 citations
Computer Science · #Advanced Text Analysis Techniques #Bayesian Modeling and Causal Inference #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Statistical Mechanics (cond-mat.stat-mech)

paper · pdf · doi:10.48550/arxiv.0905.3527

openalex publication_date 2009/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• On the product of semi-groups of operators

Cited by

• Extending Sample Persistence Variable Reduction for Constrained Combinatorial Optimization Problems
• Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering

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