  Variational Bayes (VB) inference is one of the most important algorithms in
machine learning and widely used in engineering and industry. However, VB is
known to suffer from the problem of local optima. In this Letter, we generalize
VB by using quantum mechanics, and propose a new algorithm, which we call
quantum annealing variational Bayes (QAVB) inference. We then show that QAVB
drastically improve the performance of VB by applying them to a clustering
problem described by a Gaussian mixture model. Finally, we discuss an intuitive
understanding on how QAVB works well.
