Optimal Distillation of Non-Markovianity: Bounds, Multi-Copy Gain, and the Weak-to-Essential Transition

Quantum Physics [Submitted on 14 Mar 2026] Optimal Distillation of Non-Markovianity: Bounds, Multi-Copy Gain, and the Weak-to-Essential Transition Gabriel M. Arantes, Barbara Amaral, Nadja K. Bernardes Quantum channels generally reduce the distinguishability of quantum states, limiting information transmission and processing. Previous work introduced a protocol capable of increasing the distinguishability of states after the action of a specific quantum channel. Here we show how to systematically determine the maximal distinguishability gain achievable by this method. We develop an algorithm that identifies the optimal implementation of the protocol and applies to arbitrary quantum channels in a straightforward manner. Using this approach, we demonstrate that a weakly non-Markovian channel can effectively be converted into an essentially non-Markovian one through a distillation-like process. We further analyze the quantitative features of the optimized protocol, characterizing the conditions under which the enhancement is most pronounced. Our results provide a general framework to assess and optimize distinguishability recovery in open quantum systems. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.13712 [quant-ph]   (or arXiv:2603.13712v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.13712 Focus to learn more Submission history From: Gabriel Moniz Arantes [view email] [v1] Sat, 14 Mar 2026 02:31:34 UTC (1,279 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)