Extremal distributions under approximate majorization
Michał Horodecki , Jonathan Oppenheim , Carlo Sparaciari
AbstractAlthough an input distribution may not majorize a target distribution, it may majorize a distribution which is close to the target. Here we consider a notion of approximate majorization. For any distribution, and given a distance δ, we find the approximate distributions which majorize (are majorized by) all other distributions within the distance δ. We call these the steepest and flattest approximation. This enables one to compute how close one can get to a given target distribution under a process governed by majorization. We show that the flattest and steepest approximations preserve ordering under majorization. Furthermore, we give a notion of majorization distance. This has applications ranging from thermodynamics, entanglement theory, and economics.
|Journal series||Journal of Physics A-Mathematical and Theoretical, ISSN 1751-8113, (A 25 pkt)|
|Keywords in English||majorization, state optimisation, approximate transformations|
|Score|| = 25.0, ArticleFromJournal|
= 30.0, ArticleFromJournal
|Publication indicators||: 2017 = 1.963 (2) - 2017=1.766 (5)|
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