Simulation of Open Quantum Dynamics with Bootstrap-Based Long Short-Term Memory Recurrent Neural Network

TL;DR

This paper introduces a bootstrap-enhanced LSTM neural network approach to accurately and efficiently simulate long-time open quantum system dynamics, providing confidence intervals for predictions.

Contribution

The study develops a bootstrap-based LSTM neural network method for reliable long-time quantum dynamics simulation with uncertainty quantification.

Findings

High accuracy in reproducing exact quantum evolution

Provides confidence intervals for long-term predictions

Reduces computational cost compared to traditional methods

Abstract

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.