Reverse em-problem based on Bregman divergence and its application to classical and quantum information theory

• URL: http://arxiv.org/abs/2403.09252v1
• Date: Thu, 14 Mar 2024 10:20:28 GMT
• Title: Reverse em-problem based on Bregman divergence and its application to classical and quantum information theory
• Authors: Masahito Hayashi,
• Abstract summary: Recent paper introduced an analytical method for calculating the channel capacity without the need for iteration. We turn our attention to the reverse em-problem, proposed by Toyota. We derive a non-iterative formula for the reverse em-problem.
• Score: 53.64687146666141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent paper (IEEE Trans. IT 69, 1680) introduced an analytical method for calculating the channel capacity without the need for iteration. This method has certain limitations that restrict its applicability. Furthermore, the paper does not provide an explanation as to why the channel capacity can be solved analytically in this particular case. In order to broaden the scope of this method and address its limitations, we turn our attention to the reverse em-problem, proposed by Toyota (Information Geometry, 3, 1355 (2020)). This reverse em-problem involves iteratively applying the inverse map of the em iteration to calculate the channel capacity, which represents the maximum mutual information. However, several open problems remained unresolved in Toyota's work. To overcome these challenges, we formulate the reverse em-problem based on Bregman divergence and provide solutions to these open problems. Building upon these results, we transform the reverse em-problem into em-problems and derive a non-iterative formula for the reverse em-problem. This formula can be viewed as a generalization of the aforementioned analytical calculation method. Importantly, this derivation sheds light on the information geometrical structure underlying this special case. By effectively addressing the limitations of the previous analytical method and providing a deeper understanding of the underlying information geometrical structure, our work significantly expands the applicability of the proposed method for calculating the channel capacity without iteration.

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