We propose the coarse-grained spectral projection method (CGSP), a deep learning-assisted approach for tackling quantum unitary dynamic problems with an emphasis on quench dynamics. We show CGSP can extract spectral components of many-body quantum states systematically with sophisticated neural network quantum ansatz. CGSP exploits fully the linear unitary nature of the quantum dynamics, and is potentially superior to other quantum Monte Carlo methods for ergodic dynamics. Preliminary numerical results on 1D XXZ models with periodic boundary condition are carried out to demonstrate the practicality of CGSP.
@article{arxiv.2007.09788,
title = {Coarse-grained spectral projection (CGSP): a deep learning-assisted approach to quantum unitary dynamics},
author = {Pinchen Xie and Weinan E},
journal= {arXiv preprint arXiv:2007.09788},
year = {2021}
}