  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.
