Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck

Fuente: arXiv
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Main Authors: Hayashi, Masahito, Liu, Geng
Format: Preprint
Published: 2023
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author Hayashi, Masahito
Liu, Geng
author_facet Hayashi, Masahito
Liu, Geng
contents We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al. (IEEE Trans. IT, 67, 946 (2021)) to a function defined over a set of density matrices with linear constraints so that our algorithm can be applied to optimizations of quantum operations. This algorithm has wider applicability. Hence, we apply our algorithm to the quantum information bottleneck with three quantum systems, which can be used for quantum learning. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still (Phys. Rev. A, 94, 012338 (2016)). Our numerical analysis shows that our algorithm is better than their algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2311_11188
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck
Hayashi, Masahito
Liu, Geng
Quantum Physics
We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al. (IEEE Trans. IT, 67, 946 (2021)) to a function defined over a set of density matrices with linear constraints so that our algorithm can be applied to optimizations of quantum operations. This algorithm has wider applicability. Hence, we apply our algorithm to the quantum information bottleneck with three quantum systems, which can be used for quantum learning. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still (Phys. Rev. A, 94, 012338 (2016)). Our numerical analysis shows that our algorithm is better than their algorithm.
title Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck
topic Quantum Physics
url https://arxiv.org/abs/2311.11188