论文标题
具有深神经网络的沮丧磁铁的高准确性变异性蒙特卡洛
High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks
论文作者
论文摘要
我们表明，基于非常深的（4--16层）神经网络的神经量子状态可以超过高度挫败的量子磁铁（包括量子旋转式候选者）的最先进的变化方法。我们专注于团体卷积神经网络（GCNN），使我们能够对我们的Ansätze强加空间组对称性。我们在有序和旋转液体阶段的正方形和三角形晶格上的$ J_1-J_2 $ HEISENBERG型号的$ J_1-J_2 $ HEISENBERG模型中实现了最先进的地面能量，并讨论了在非平凡对称性扇区中访问低亮态状态的方法。我们还计算三角形晶格上量子磁磁相的自旋和二聚体相关函数，这些函数并不表示常规或价值键的排序。
We show that neural quantum states based on very deep (4--16-layered) neural networks can outperform state-of-the-art variational approaches on highly frustrated quantum magnets, including quantum-spin-liquid candidates. We focus on group convolutional neural networks (GCNNs) that allow us to impose space-group symmetries on our ansätze. We achieve state-of-the-art ground-state energies for the $J_1-J_2$ Heisenberg models on the square and triangular lattices, in both ordered and spin-liquid phases, and discuss ways to access low-lying excited states in nontrivial symmetry sectors. We also compute spin and dimer correlation functions for the quantum paramagnetic phase on the triangular lattice, which do not indicate either conventional or valence-bond ordering.
