Coarse-grained spectral projection (CGSP): a deep learning-assisted approach to quantum unitary dynamics
This work addresses quantum dynamics problems for researchers in quantum physics and computational science, but it appears incremental as it builds on existing methods with a new hybrid approach.
The authors tackled the problem of extracting spectral components in quantum unitary dynamics, particularly for quench dynamics, by proposing the coarse-grained spectral projection (CGSP) method, which uses deep learning and neural network quantum ansatz, and demonstrated its practicality on 1D XXZ models with periodic boundary conditions.
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.