论文标题
二维无旋转晶格费米的阶段，具有第一量化的深神经网络量子
Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
论文作者
论文摘要
开发了初步的深度神经网络技术，用于分析晶格上强烈耦合的费米子系统。使用Slater-Jastrow启发了ANSATZ，该Ansatz利用了具有卷积残留块的深层残留网络，我们大致确定了与最近邻居相互作用的平方晶格上无旋转费米子的基态。与精确的对角线化相比，在能量和相关功能上，神经网络ANSATZ的灵活性与精确的对角线化相比，具有很高的准确性。在大型系统上，我们获得了金属和电荷有序相之间边界的准确估计，这是相互作用强度和粒子密度的函数。
First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice. Using a Slater-Jastrow inspired ansatz which exploits deep residual networks with convolutional residual blocks, we approximately determine the ground state of spinless fermions on a square lattice with nearest-neighbor interactions. The flexibility of the neural-network ansatz results in a high level of accuracy when compared to exact diagonalization results on small systems, both for energy and correlation functions. On large systems, we obtain accurate estimates of the boundaries between metallic and charge ordered phases as a function of the interaction strength and the particle density.
