Institut für Angewandte Physik, Technische Universität Darmstadt, Hochschulstrasse 6, 64289 Darmstadt, Germany.
Phys Rev Lett. 2013 Jul 19;111(3):033601. doi: 10.1103/PhysRevLett.111.033601. Epub 2013 Jul 15.
The maximal storage duration is an important benchmark for memories. In quantized media, storage times are typically limited due to stochastic interactions with the environment. Also, optical memories based on electromagnetically induced transparency (EIT) suffer strongly from such decoherent effects. External magnetic control fields may reduce decoherence and increase EIT storage times considerably but also lead to complicated multilevel structures. These are hard to prepare perfectly in order to push storage times toward the theoretical limit, i.e., the population lifetime T(1). We present a self-learning evolutionary strategy to efficiently drive an EIT-based memory. By combination of the self-learning loop for optimized optical preparation and improved dynamical decoupling, we extend EIT storage times in a doped solid above 40 s. Moreover, we demonstrate storage of images by EIT for 1 min. These ultralong storage times set a new benchmark for EIT-based memories. The concepts are also applicable to other storage protocols.
最大存储时间是存储器的一个重要基准。在量化介质中,由于与环境的随机相互作用,存储时间通常受到限制。此外,基于电磁感应透明(EIT)的光学存储器也受到这种退相干效应的严重影响。外部磁场控制场可以减少退相干并大大增加 EIT 的存储时间,但也会导致复杂的多级结构。为了将存储时间推向理论极限,即粒子数布居寿命 T(1),很难完美地准备这些结构。我们提出了一种自学习进化策略,以有效地驱动基于 EIT 的存储器。通过优化光学制备和改进动态解耦的自学习循环,我们将掺杂固体中的 EIT 存储时间延长至 40 秒以上。此外,我们还演示了 EIT 存储 1 分钟的图像。这些超长的存储时间为基于 EIT 的存储器设定了新的基准。这些概念也适用于其他存储协议。