Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck
arXiv:2311.11188 · doi:10.1088/2058-9565/ad6eb1
Abstract
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.
References in corpus (10)
- The information bottleneck method
- Generalization in Quantum Machine Learning: a Quantum Information Perspective
- Computing Quantum Channel Capacities
- Convexity and Operational Interpretation of the Quantum Information Bottleneck Function
- Quantum Rate-Distortion Coding of Relevant Information
- Bregman divergence based em algorithm and its application to classical and quantum rate distortion theory
- Efficient algorithms for quantum information bottleneck
- Quantum Predictive Filtering
- Efficient Computation of the Quantum Rate-Distortion Function
- Iterative minimization algorithm on a mixture family