论文标题
在非马克维亚环境中充电量子电池：碰撞模型方法
Charging a quantum battery in a non-Markovian environment: a collisional model approach
论文作者
论文摘要
我们研究了非马克维亚性在开放系统量子电池的充电过程中的影响。我们采用碰撞模型框架，在该框架中，可以通过允许这些杂物相互作用来引入动态中的一组离散辅助系统和内存效应来描述环境。我们详细研究了稳态麦内氏疗法的行为以及信息回流向系统对表征充电过程的不同特征的影响。值得注意的是，我们发现可实现的麦内氏综述的最大值：可以在存在无内存环境的情况下获得此值，但只有在大损失极限下，如[D. D. Farina等人，物理。 Rev. B 99，035421（2019）]，或在有内存的环境的情况下，也超出了大损失极限。通常，我们表明，具有内存的环境的存在使我们能够在参数空间中更大的区域生成接近其最大值的稳态麦芽糖，因此可能在较短的时间内。依靠非马克维亚性的几何量度，我们表明，在有和没有记忆的环境的情况下，当电池动力学的非马克维亚度为零时，可获得最大的麦加旋转，这可能是由于分别由不整数的相互作用在记忆效应之间产生的结果，分别是由于分别由环境，环境，环境和架子连接到电池的效果。
We study the effect of non-Markovianity in the charging process of an open-system quantum battery. We employ a 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 and the impact of the information backflow to the system on the different features characterizing the charging process. Remarkably, we find that there is a maximum value of the ergotropy achievable: this value can be obtained either in the presence of memoryless environment, but only in the large-loss limit, as derived in [D. Farina et al., Phys. Rev. B 99, 035421 (2019)], or in the presence of an environment with memory also beyond the large-loss limit. In general, we show that the presence of an environment with memory allows us to generate steady-state ergotropy near to its maximum value for a much larger region in the parameter space and thus potentially in a shorter time. Relying on the geometrical measure of non-Markovianity, we show that in both the cases of an environment with and without memory the ergotropy maximum is obtained when the non-Markovianity of the dynamics of the battery is zero, possibly as the result of a non-trivial interplay between the memory effects induced by, respectively, the environment and the charger connected to the battery.
