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

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This study introduces a bootstrap-based long short-term memory neural network (LSTM-NN) for simulating open quantum systems. The method accurately predicts long-time quantum dynamics with quantifiable uncertainty.

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Area of Science:

  • Quantum Dynamics
  • Computational Physics
  • Machine Learning in Science

Background:

  • Simulating long-time dynamics of open quantum systems is computationally challenging.
  • Existing methods often struggle with accuracy and efficiency for extended time scales.

Purpose of the Study:

  • To develop a novel, accurate, and computationally efficient method for simulating long-time quantum dynamics.
  • To introduce a bootstrap-based long short-term memory neural network (LSTM-NN) approach for this purpose.

Main Methods:

  • Utilized a long short-term memory neural network (LSTM-NN) ensemble.
  • Employed the bootstrap method for LSTM-NN construction and prediction, enabling Monte Carlo estimation of confidence intervals.
  • Resampled time-series sequences from numerically exact multilayer multiconfigurational time-dependent Hartree (MCTDH) method for training.

Main Results:

  • The LSTM-NN ensemble accurately simulated the long-time quantum dynamics of open systems.
  • Simulated results showed high consistency with exact quantum evolution.
  • The approach provided forecasting uncertainty, reflecting prediction reliability.

Conclusions:

  • The bootstrap-based LSTM-NN approach is a practical and powerful tool for simulating long-time quantum dynamics.
  • Achieved high accuracy with low computational cost.
  • Quantified prediction uncertainty for enhanced reliability.