论文标题
开放量子系统动力学的一声轨迹学习
One-shot trajectory learning of open quantum systems dynamics
论文作者
论文摘要
非绝热的量子动态对于理解轻度收获过程很重要，但是它们使用传统方法的传播可能非常昂贵。在这里，我们提出了一种单发轨迹学习方法，该方法允许直接对新的仿真参数（例如温度和重组能量）进行简化的密度矩阵的整个轨迹进行超快速预测。正如我们在相对较大的量子系统（Fenna-Matthews-Olsen（FMO）复合物）上所证明的那样，整个10PS长的繁殖需要70毫秒。我们的方法还大大减少了培训的时间和记忆要求。
Nonadiabatic quantum dynamics are important for understanding light-harvesting processes, but their propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows to directly make ultra-fast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10ps long propagation takes 70 milliseconds as we demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.
