Quantum annealing for variational Bayes inference

Authors:
Issei Sato;Kenichi Kurihara;Shu Tanaka;Hiroshi Nakagawa;Seiji Miyashita
Affiliations:
University of Tokyo, Japan;Google, Tokyo, Japan;University of Tokyo, Japan;The University of Tokyo, Japan;The University of Tokyo, Japan
Venue:
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Year:
2009

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Abstract

This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local optimum than SAVB in terms of the variational free energy in latent Dirichlet allocation (LDA).