Grassmann代数与张量网络——研究强关联费米模型的崭新数值方法  

Combining the Grassmann algebra and tensor network: a new method to study strongly correlated fermionic systems

作　　者：娄捷[1] LOU Jie(Department of Physics, Fudan University, Shanghai 200433, Chin)

机构地区：[1]复旦大学物理系,上海200433

出　　处：《物理》2017年第7期439-445,共7页Physics

摘　　要：文章介绍了把Grassmann代数与张量网络/张量矩阵乘积态类数值方法相结合所发展出的崭新的处理强关联费米/电子模型的严格数值模拟方法。该类方法普适、高效、严格,是具有广阔发展前景的研究手段。文章还介绍了GMERA(Grassmann multi-scale entanglement renormalization ansatz)方法的一些验证结果以及对t-J模型进行模拟的最新进展。We introduce a new kind of numerical method to treat strongly correlated fcnnionic/electronic systems. By combining Grassmann algebra with tensor network/tensor product state methods, we may obtain methods that are general unbiased, and highly effective. We will show benchmark GMERA calculations and new devekpments in the study of the t-J model.

关 键 词：张量网络 张量矩阵乘积态 GRASSMANN代数 GMERA T-J模型 D波超导 

分 类 号：O469[理学—凝聚态物理]