论文标题
改进了包括费米斯（费米）的二维张量网络上的粗晶
Improved coarse-graining methods on two dimensional tensor networks including fermions
论文作者
论文摘要
我们展示了如何应用重新归一化组算法，其中包含纠缠过滤方法和环路优化到张量网络，该张量网络包括Grassmann变量，这些变量代表基础晶格场理论中的费米子。作为数值测试，计算了针对二维Wilson-Majorana费米子和两种风味毛的模型计算的多种数量。改进的算法对于诸如自由能和Fisher零的测定等数量的精度表现出更好的准确性。
We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson--Majorana fermions and for the two flavor Gross--Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher's zeros.
