Coarse-grained spectral projection (CGSP): a deep learning-assisted approach to quantum unitary dynamics

TL;DR

The paper introduces CGSP, a deep learning-assisted spectral method for analyzing quantum unitary dynamics, especially quench dynamics, demonstrating its effectiveness on 1D XXZ models.

Contribution

CGSP is a novel spectral projection method that leverages neural networks to extract spectral components in quantum dynamics, outperforming some existing methods.

Findings

Successfully applied to 1D XXZ models with periodic boundary conditions.

Demonstrates potential advantages over quantum Monte Carlo methods.

Shows practical feasibility of the approach.

Abstract

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.