A Quantum Extension of Variational Bayes Inference

TL;DR

This paper introduces a quantum-inspired extension to Variational Bayes inference, called QAVB, which significantly improves clustering performance by leveraging quantum annealing principles.

Contribution

The paper proposes the QAVB algorithm, a novel quantum-inspired approach that enhances traditional VB inference and mitigates local optima issues.

Findings

QAVB outperforms classical VB in clustering tasks

QAVB reduces local optima problems in inference

Quantum annealing principles improve inference quality

Abstract

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.