2019
DOI: 10.3390/e21101020
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Quasi-Entropies and Non-Markovianity

Abstract: We address an informational puzzle that appears with a non-Markovian open qubit dynamics: namely the fact that, while, according to the existing witnesses of information flows, a single qubit affected by that dissipative dynamics does not show information returning to it from its environment, instead two qubits do show such information when evolving independently under the same dynamics. We solve the puzzle by adding the so-called quasi-entropies to the possible witnesses of information flows.

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Cited by 3 publications
(6 citation statements)
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“…. ., N − 1 make the unique (N 2 − 1) × (N 2 − 1) Kossakowski matrix K. The interest in this matrix is that, for Markovian dynamics, its PSD is equivalent to the condition of CP of the map [20,21], while for non-Markovian dynamics, it is only a sufficient condition [78]. But here, we are primarily interested in its uniqueness property.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…. ., N − 1 make the unique (N 2 − 1) × (N 2 − 1) Kossakowski matrix K. The interest in this matrix is that, for Markovian dynamics, its PSD is equivalent to the condition of CP of the map [20,21], while for non-Markovian dynamics, it is only a sufficient condition [78]. But here, we are primarily interested in its uniqueness property.…”
Section: Discussionmentioning
confidence: 99%
“…The inequality (78) relates the accuracy of the geometric-arithmetic approximation of the spectral density mean, to the decay rate of the s-values of the X-matrix, |x n |. Assume that |x n | decays geometrically.…”
Section: The Position-displacement Superoperatorsmentioning
confidence: 99%
“…The remaining terms, e.g., L ni,mj (t), n = i, m = j; c ni,p (t), n = i, p = 1, ..., N − 1; c p,ni (t), n = i, p = 1, ..., N −1; and c pq (t), p, q = 1, ..., N −1 make the unique (N 2 − 1) × (N 2 − 1) Kossakowski matrix K. The interest in this matrix is that, for Markovian dynamics, its PSD is equivalent to the condition of CP of the map [8,9], while for non-Markovian dynamics, it is only a sufficient condition [36]. But here, we are primarily interested in its uniqueness property.…”
Section: B Superoperator Representationmentioning
confidence: 99%
“…In the pre-Markovian stage where the ME is time-dependent, the answer is that the PSD of the timedependent Kossakowski matrix is sufficient, but not necessary condition for the CP of the map; see Ref. [36] for examples of this and the discussion of the related intertwining maps. The latter are composite maps: the states of the system at time t 1 > 0 are sent to the initial factorized state, by the inverse map (assuming that it exists), which is then sent to the state at time t 2 > t 1 by the time-forward map.…”
Section: Markovian Dynamics As An Intertwining Mapmentioning
confidence: 99%
“…We emphasize that our procedure can be applied not only to the standard Redfield equation but also to its version with time-dependent coefficients (that can result, for instance, from avoiding the so-called "second Markov" approximation). In this case, our regularization preserves the time dependence of the coefficients but makes the dynamics completely positive divisible [29][30][31]. The residual time dependence is an indicator that our approach could perform better at short times with respect to existing schemes.…”
Section: Introductionmentioning
confidence: 96%