Quantifying non-Markovianity: a quantum resource-theoretic approach
ORAL
Abstract
We study quantum non-Markovianity as a resource theory and introduce the robustness of non-Markovianity: an operationally-motivated, optimization-free measure that quantifies the minimum amount of Markovian noise that can be mixed with a non-Markovian evolution before it becomes Markovian. We show that this quantity is a bonafide non-Markovianity measure since it is faithful, convex, and monotonic under composition with Markovian maps. A two-fold operational interpretation of this measure is provided, with the robustness measure quantifying an advantage in both state and channel discrimination tasks. Moreover, we connect the robustness measure to single-shot information theory by using it to upper bound the min-accessible information of a non-Markovian map. Furthermore, we provide a closed-form analytical expression for this measure and show that, quite remarkably, the robustness measure is exactly equal to half the Rivas-Huelga-Plenio (RHP) measure [Phys. Rev. Lett. 105, 050403 (2010)]. As a result, we provide a direct operational meaning to the RHP measure while endowing the robustness measure with the physical characterizations of the RHP measure.
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Presenters
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Namit Anand
Univ of Southern California
Authors
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Namit Anand
Univ of Southern California
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Todd Brun
Univ of Southern California