Quantum Annealing for Variational Bayes Inference
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).
Cite
Text
Sato et al. "Quantum Annealing for Variational Bayes Inference." Conference on Uncertainty in Artificial Intelligence, 2009.Markdown
[Sato et al. "Quantum Annealing for Variational Bayes Inference." Conference on Uncertainty in Artificial Intelligence, 2009.](https://mlanthology.org/uai/2009/sato2009uai-quantum/)BibTeX
@inproceedings{sato2009uai-quantum,
title = {{Quantum Annealing for Variational Bayes Inference}},
author = {Sato, Issei and Kurihara, Kenichi and Tanaka, Shu and Nakagawa, Hiroshi and Miyashita, Seiji},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {479-486},
url = {https://mlanthology.org/uai/2009/sato2009uai-quantum/}
}