Pure non-Markovian evolutions
ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
Scuola Normale Superiore, I-56126 Pisa, Italy
Published: | 2023-09-26, volume 7, page 1124 |
Eprint: | arXiv:2302.04883v2 |
Doi: | https://doi.org/10.22331/q-2023-09-26-1124 |
Citation: | Quantum 7, 1124 (2023). |
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Abstract
Non-Markovian dynamics are characterized by information backflows, where the evolving open quantum system retrieves part of the information previously lost in the environment. Hence, the very definition of non-Markovianity implies an initial time interval when the evolution is noisy, otherwise no backflow could take place. We identify two types of initial noise, where the first has the only effect of degrading the information content of the system, while the latter is essential for the appearance of non-Markovian phenomena. Therefore, all non-Markovian evolutions can be divided into two classes: noisy non-Markovian (NNM), showing both types of noise, and pure non-Markovian (PNM), implementing solely essential noise. We make this distinction through a timing analysis of fundamental non-Markovian features. First, we prove that all NNM dynamics can be simulated through a Markovian pre-processing of a PNM core. We quantify the gains in terms of information backflows and non-Markovianity measures provided by PNM evolutions. Similarly, we study how the entanglement breaking property behaves in this framework and we discuss a technique to activate correlation backflows. Finally, we show the applicability of our results through the study of several well-know dynamical models.
![](https://quantum-journal.org/wp-content/uploads/2023/09/Q1-1-scaled.jpg)
Featured image: Top: We identify two types of initial noise for non-Markovian (NM) evolutions: useless noise, which solely degrades the information content of the system, and essential noise, which is necessary for the appearance of NM phenomena, namely information backflows. Hence, we divide NM evolutions into noisy non-Markovian (NNM), showing both types of noise, and pure non-Markovian (PNM), implementing only essential noise. Bottom: NNM evolutions $\mathbf \Lambda$ can be simulated as follows. The system interacts with a first environment $E_1$ and information is lost monotonically. This Markovian pre-processing coincides with the useless noise of $\mathbf \Lambda$. Later, $E_2$ is discarded, the system evolves while interacting with $E_2$ and NM phenomena are observed. This stage corresponds to the PNM core $\overline{\mathbf \Lambda}$ of $\mathbf \Lambda$. Information losses are represented in blue and backflows in red.
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