Quantum Variational Bayes on Manifolds

Abstract

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. VB algorithms are generally restricted to the case where the variational parameter space is Euclidean. Recently, the VB framework has been extended to the case where the variational parameter space is a Riemannian manifold. Such a manifold-based algorithm, by utilizing the natural gradient, can exploit the geometric structures to derive an efficient VB procedure. However, natural gradient in high dimension can be prohibitively computationally expensive to compute. This work proposes using a quantum natural gradient within the manifold VB framework. We demonstrate that the quantum natural gradient does not suffer from the well-known quantum-classical readout computational bottleneck, and hence delivers a highly computational efficiency. The resulting quantum manifold VB algorithm is provably convergent.