A Quantum Extension of Variational Bayes Inference
This addresses a key bottleneck in machine learning for practitioners using Variational Bayes, though it appears incremental as it builds directly on existing methods.
The authors tackled the problem of local optima in Variational Bayes inference by generalizing it with quantum mechanics to propose quantum annealing variational Bayes, which drastically improved performance in a Gaussian mixture model clustering problem.
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.