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In this communication we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system-bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems, based on kernel ridge regression.

In this Article we perform a systematic study of the global entanglement and exciton coherence length dynamics coherence in natural light-harvesting system Fenna-Matthews-Olson (FMO) complex across various parameters of a dissipative environment from low to high temperatures, weak to strong system-environment coupling, and non-Markovian environments. A non-perturbative numerically exact hierarchical equations of motions method is employed to propagate the dynamics of the system. We found that entanglement is driven primarily by the strength of interaction between the system and environment, and it is modulated by the interplay between temperature and non-Markovianity. In contrast, coherence length is found to be insensitive to non-Markovianity. Furthermore, our results do not show the direct correlation between global entanglement and the efficiency of the excitation energy transfer in the FMO complex

Simulations of the dynamics of dissipative quantum systems utilize many methods such as physics-based quantum, semiclassical, and quantum-classical as well as machine learning-based approximations, development and testing of which requires diverse data sets. Here we present a new database QD3SET-1 containing eight data sets of quantum dynamical data for two systems of broad interest, spin-boson (SB) model and the Fenna--Matthews--Olson (FMO) complex, generated with two different methods solving the dynamics, approximate local thermalizing Lindblad master equation (LTLME) and highly accurate hierarchy equations of motion (HEOM). One data set was generated with the SB model which is a two-level quantum system coupled to a harmonic environment using HEOM for 1,000 model parameters. Seven data sets were collected for the FMO complex of different sizes(7- and 8-site monomer and 24-site trimer with LTLME and 8-site monomer with HEOM) for 500--879 model parameters. Our QD3SET-1 database contains both population and coherence dynamics data and part of it has been already used for machine learning-based quantum dynamics studies., Comment: 14 pages, 3 figures

It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feed-forward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional Gated Recurrent Unit model is found to be the most efficient ANN model., Comment: 22 pages, 6 figures