Quantum-mechanical extension of variational Bayes inference for unsupervised and supervised learning

Abstract

Machine learning has attracted much attention in science and engineering. In particular, Bayes estimation is one of the most important methods in machine learning and variational Bayes （VB） inference is a widely used algorithm for it. On the other hand, computations based on quantum mechanics also have attracted much attention since they are expected to lead to a breakthrough. In this review paper, we explain a recently proposed quantum-mechanical extension of VB. We also discuss two kinds of problem settings: unsupervised and supervised learning.