Quantum annealing for variational Bayes inference

Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)


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).

Original languageEnglish
Number of pages8
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada
Duration: 2009 Jun 182009 Jun 21


Conference25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
CityMontreal, QC

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Quantum annealing for variational Bayes inference'. Together they form a unique fingerprint.

Cite this