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

Abstract Quantum information bottleneck was proposed by Grimsmo and Still (2016 Phys. Rev. A 94 012338) as a promising method for quantum supervised machine learning. To study this method, we generalize the quantum Arimoto–Blahut algorithm by Ramakrishnan et al (2021 IEEE Trans. Inf. Theory 67 946) 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, and we apply our algorithm to the quantum information bottleneck with three quantum systems. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still. Our numerical analysis shows that our algorithm is better than their algorithm.