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

Asaduzzaman, Muhammad, Catterall, Simon, Meurice, Yannick, Sakai, Ryo, Toga, Goksu Can

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.

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