Recovery map stability for the Data Processing Inequality
Eric A. Carlen, Anna Vershynina

TL;DR
This paper establishes a quantitative stability bound for the Data Processing Inequality using the Petz recovery map, providing explicit conditions for equality and detailed descriptions of the solution set.
Contribution
It introduces a new stability bound for the DPI involving the Petz recovery map and characterizes the solutions of the Petz equation in detail.
Findings
Derived a lower bound for the DPI gap involving the Petz recovery map.
Provided a detailed description of the fixed points of the coarse graining map.
Extended results to various quasi-relative entropies.
Abstract
The Data Processing Inequality (DPI) says that the Umegaki relative entropy is non-increasing under the action of completely positive trace preserving (CPTP) maps. Let be a finite dimensional von Neumann algebra and a von Neumann subalgebra if it. Let be the tracial conditional expectation from onto . For density matrices and in , let and . Since is CPTP, the DPI says that , and the general case is readily deduced from this. A theorem of Petz says that there is equality if and only if , where…
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