论文标题
粗粒光谱投影（CGSP）：量子统一动力学的深度学习辅助方法
Coarse-grained spectral projection (CGSP): a deep learning-assisted approach to quantum unitary dynamics
论文作者
论文摘要
我们提出了粗粒光谱投影方法（CGSP），这是一种深入学习辅助方法，用于解决量子统一动态问题，重点是淬灭动力学。我们显示，CGSP可以系统地提取多体量子状态的光谱成分，并具有复杂的神经网络量子Ansatz。 CGSP充分利用量子动力学的线性单位性质，并且可能优于其他量子蒙特卡洛方法用于沿阵行动力学。对具有周期性边界条件的1D XXZ模型进行初步数值结果，以证明CGSP的实用性。
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.
