Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach

Abstract

This article proposes circular hidden quantum Markov models (c-HQMMs), which can be applied for modeling temporal data. We show that c-HQMMs are equivalent to a tensor network (more precisely, circular local purified state) model. This equivalence enables us to provide an efficient learning model for c-HQMMs. The proposed learning approach is evaluated on six real datasets and demonstrates the advantage of c-HQMMs as compared to HQMMs and HMMs.