Daniele Morrone

University of Milan, Italy

14 February 2023 Tue 5 pm

                                      IBS Center for Theoretical Physics of Complex Systems (PCS), Administrative Office (B349), Theory Wing, 3rd floor

                                      Expo-ro 55, Yuseong-gu, Daejeon, South Korea, 34126 Tel: +82-42-878-8633                     

We study the effect of non-Markovianity in the charging process of an open-system quantum battery. We employ collisional model framework, where the environment is described by a discrete set of ancillary systems and memory effects in the dynamics can be introduced by allowing these ancillas to interact. We study in detail the behaviour of the steady-state ergotropy, the maximum amount of energy that can be extracted from a unitary operation, and the non-trivial interplay between the parameter controlling the information backflow of the environment and the other parameters characterizing the dynamics. Remarkably, we find that there is a maximum value of the ergotropy

achievable: This value can be obtained either in the Markovian case, but only in the large-loss limit, as derived in [2], or in the presence of a non-Markovian environment also beyond the large-loss limit. In general, we show that the presence of environment with memory allows us to generate ergotropy near to its maximum value for a much larger region in the parameter space. Finally, we discuss in detail the role of non-Markovianity by evaluating the non-Markovianity measure for the different subsystems composing our quantum battery model. We remarkably find that the maximum value of ergotropy is in general obtained when the non-Markovianity for the battery subsystem only is close to zero, regardless the memory properties of the environment interacting with the charger.


[1] D. Morrone, M.A.C. Rossi, A. Smirne, M.G. Genoni, arXiv:2212.13488

[2] D. Farina, G. M. Andolina, A. Mari, M. Polini, and V. Giovannetti, Physical Review B 99, 035421 (2019), arXiv:1810.10890

  1. Charging a quantum battery in a non-markovian environment: a collisional model approach