Abstract
Using the paradigm of information backflow to characterize a non-Markovian evolution, we introduce so-called precursors of non-Markovianity, i.e. necessary properties that the system and environment state must exhibit at earlier times in order for an ensuing dynamics to be non-Markovian. In particular, we consider a quantitative framework to assess the role that established system-environment correlations together with changes in environmental states play in an emerging non-Markovian dynamics. By defining the relevant contributions in terms of the Bures distance, which is conveniently expressed by means of the quantum state fidelity, these quantities are well defined and easily applicable to a wide range of physical settings. We exemplify this by studying our precursors of non-Markovianity in discrete and continuous variable non-Markovian collision models.
Highlights
Open quantum systems provide the framework for describing how a system of interest interacts with its surroundings
In the context of open quantum systems, typically the system of interest is much smaller than its environment and in this case, if the coupling is weak, the dynamics of the system can be well captured by a memoryless or Markovian evolution
While considerably less well studied, the case of continuous variable (CV) collision models provides an interesting platform for the realization and study of non-Markovianity, in light of the remarkable advances in the manipulation of CV systems for simulating open quantum system dynamics [54,55,56]
Summary
Any further distribution of environment state must exhibit at earlier times in order for an ensuing dynamics to be nonthis work must maintain attribution to the Markovian. By defining the relevant contributions in terms of the Bures distance, which is conveniently expressed by means of the quantum state fidelity, these quantities are well defined and applicable to a wide range of physical settings. We exemplify this by studying our precursors of non-Markovianity in discrete and continuous variable non-Markovian collision models
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