Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering

TL;DR

This paper introduces a quantum annealing algorithm tailored for Dirichlet process mixture models, enabling efficient network clustering by handling an unfixed number of classes, and demonstrates its superiority over traditional methods.

Contribution

The paper presents a novel quantum annealing algorithm that extends existing frameworks to accommodate Dirichlet process models with variable class counts, applicable to network clustering.

Findings

QA outperforms SA, MCMC, and beam search in finding MAP estimates.

QA is easy to implement, comparable to simulated annealing.

Experimental results show QA's effectiveness in network clustering.

Abstract

We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA), i.e., it is a parallel stochastic optimization technique. Existing approaches [Kurihara et al. UAI2009, Sato et al. UAI2009] and cannot be applied to the CRP because their QA framework is formulated using a fixed number of mixture components. The proposed QA algorithm can handle an unfixed number of classes in mixture models. We applied QA to a DPM model for clustering vertices in a network where a CRP seating arrangement indicates a network partition. A multi core processor was used for running QA in experiments, the results of which show that QA is better than SA, Markov chain Monte Carlo inference, and beam search at finding a maximum a posteriori estimation of a seating arrangement in the CRP. Since our QA algorithm is as easy as to implement the SA algorithm, it is suitable for a wide range of applications.