Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
Quantum Physics
2021-11-03 v1 Machine Learning
Abstract
In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case). We show that c-HQMMs are equivalent to a constrained tensor network (more precisely, circular Local Purified State with positive-semidefinite decomposition) 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 on multiple datasets as compared to HQMMs, circular HMMs, and HMMs.
Cite
@article{arxiv.2111.01536,
title = {Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach},
author = {Mohammad Ali Javidian and Vaneet Aggarwal and Zubin Jacob},
journal= {arXiv preprint arXiv:2111.01536},
year = {2021}
}