Two-Dimensional Convolutional Neural Networks for Predicting Non-Markovian Quantum Dissipative Dynamics (Communications in Computational Chemistry, 2025)
Simulating non-Markovian quantum dissipative dynamics remains a major challenge in theoretical and computationalchemistry. While traditional numerical methods such as hierarchical equations of motion are numerically exact, they suffer fromprohibitive computational costs when modeling systems with complex environmental couplings or strong non-Markovian effects.To address this limitation, we propose a deep learning framework based on two-dimensional convolutional neural networks (2D-CNN) for efficiently predicting long-time quantum dissipative dynamics using only short-time trajectory data. Our approachincorporates a 1D-to-2D feature reconstruction strategy, which transforms 1D time-series data into 2D images, and a multi-timescale fusion network to resolve complex dynamical features. We validate the framework on two paradigmatic cases — dissipative relaxation in a two-level system and Rabi oscillations in a dissipative spin system — achieving prediction mean absoluteerrors of $10^{-3}$ and $10^{-2},$ respectively. The results highlight the effectiveness of our 2D-CNN approach in capturing long-timetemporal correlations, providing a computationally efficient pathway for simulating quantum dynamics in realistic open systems.