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A minimax approach to one-shot entropy inequalities

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Title: A minimax approach to one-shot entropy inequalities
Authors: Anshu, A
Berta, M
Jain, R
Tomamichel, M
Item Type: Journal Article
Abstract: One-shot information theory entertains a plethora of entropic quantities, such as the smooth max-divergence, hypothesis testing divergence, and information spectrum divergence, that characterize various operational tasks in quantum information theory and are used to analyze their asymptotic behavior. Tight inequalities between these quantities are thus of immediate interest. In this note, we use a minimax approach (appearing previously, for example, in the proofs of the quantum substate theorem), to simplify the quantum problem to a commutative one, which allows us to derive such inequalities. Our derivations are conceptually different from previous arguments and in some cases lead to tighter relations. We hope that the approach discussed here can lead to progress in open problems in quantum Shannon theory and exemplify this by applying it to a simple case of the joint smoothing problem.
Issue Date: 1-Dec-2019
Date of Acceptance: 14-Oct-2019
URI: http://hdl.handle.net/10044/1/91892
DOI: 10.1063/1.5126723
ISSN: 0022-2488
Publisher: American Institute of Physics
Start Page: 1
End Page: 7
Journal / Book Title: Journal of Mathematical Physics
Volume: 60
Issue: 12
Copyright Statement: © 2019 Author(s). This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in J. Math. Phys. 60, 122201 (2019); doi: 10.1063/1.5126723 and may be found at https://doi.org/10.1063/1.5126723
Keywords: Science & Technology
Physical Sciences
Physics, Mathematical
Physics
Science & Technology
Physical Sciences
Physics, Mathematical
Physics
quant-ph
quant-ph
01 Mathematical Sciences
02 Physical Sciences
Mathematical Physics
Publication Status: Published
Article Number: ARTN 122201
Online Publication Date: 2019-12-04
Appears in Collections:Computing