Generalized quantum ArimotoBlahut algorithm and its application to quantum information bottleneck
Abstract
We generalize the quantum ArimotoBlahut 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.
 Publication:

arXiv eprints
 Pub Date:
 November 2023
 DOI:
 10.48550/arXiv.2311.11188
 arXiv:
 arXiv:2311.11188
 Bibcode:
 2023arXiv231111188H
 Keywords:

 Quantum Physics
 EPrint:
 Quantum Science and Technology, 9, 045036 (2024)