Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
复制标题
具有第一量子化深度神经网络量子态的二维无自旋晶格费米子的相位
DOI：
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发表时间：
2020
影响因子：
3.7
通讯作者：
Giuseppe Carleo
中科院分区：
文献类型：
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作者：
J. Stokes;Javier Robledo Moreno;E. Pnevmatikakis;Giuseppe Carleo
开发了第一个量化深度神经网络技术，用于分析晶格上的强耦合费米子系统。使用受 Slater-Jastrow 启发的 ansatz（利用具有卷积残差块的深度残差网络），我们近似确定具有最近邻相互作用的方晶格上无自旋费米子的基态。与小型系统上的精确对角化结果相比，神经网络 ansatz 的灵活性导致了能量和相关函数的高精度。在大型系统中，我们获得了金属相和电荷有序相之间边界的准确估计，作为相互作用强度和颗粒密度的函数。
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.