Factor Graphs for Quantum Information Processing
Abstract
[...] In this thesis, we are interested in generalizing factor graphs and the relevant methods toward describing quantum systems. Two generalizations of classical graphical models are investigated, namely double-edge factor graphs (DeFGs) and quantum factor graphs (QFGs). Conventionally, a factor in a factor graph represents a nonnegative real-valued local functions. Two different approaches to generalize factors in classical factor graphs yield DeFGs and QFGs, respectively. We proposed/re-proposed and analyzed generalized versions of belief-propagation algorithms for DeFGs/QFGs. As a particular application of the DeFGs, we investigate the information rate and their upper/lower bounds of classical communications over quantum channels with memory. In this study, we also propose a data-driven method for optimizing the upper/lower bounds on information rate.
Cite
@article{arxiv.2203.12413,
title = {Factor Graphs for Quantum Information Processing},
author = {Michael X. Cao},
journal= {arXiv preprint arXiv:2203.12413},
year = {2022}
}
Comments
This is the finial version of the thesis of Michael X. Cao submitted in April 2021 in partial fulfillment of the requirements for the degree of doctor of philosophy in information engineering at the Chinese university of Hong Kong