基于Bootstrap的长短期记忆循环神经网络模拟开放量子动力学
Simulation of Open Quantum Dynamics with Bootstrap-Based Long Short-Term Memory Recurrent Neural Network
摘要：
利用带有长短期记忆单元的循环神经网络（LSTM-NN）模拟了开放量子系统的长时间动力学行为。将Bootstrap方法应用于LSTM-NN的构造和预测，给出了预测置信区间的Monte-Carlo估计。在这种方法中，大量的LSTM-NN构造从早期阶段的量子演化的数值精确多层多组态含时Hartree方法得到的响应时间序列。将所建立的LSTM-NN系综用于长时间量子动力学的可靠传播，模拟结果与精确演化高度一致。给出了部分反映LSTM-NN预测可靠性的预测不确定度。这表明基于Bootstrap的LSTM-NN方法是一种实用而强大的工具，可以以高精度和低计算成本传播开放系统的长时间量子动力学。
英文摘要：
The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed to simulate the long-time dynamics of open quantum system. The bootstrap method is applied in the LSTM-NN construction and prediction, which provides a Monte-Carlo estimation of forecasting confidence interval. Within this approach, a large number of LSTM-NNs are constructed by resampling time-series sequences that were obtained from the early-stage quantum evolution given by numerically-exact multilayer multiconfigurational time-dependent Hartree method. The built LSTM-NN ensemble is used for the reliable propagation of the long-time quantum dynamics and the simulated result is highly consistent with the exact evolution. The forecasting uncertainty that partially reflects the reliability of the LSTM-NN prediction is also given. This demonstrates the bootstrap-based LSTM-NN approach is a practical and powerful tool to propagate the long-time quantum dynamics of open systems with high accuracy and low computational cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
