基于张量网络算法的自旋梯子系统的弦序参量的研究
String order parameters of tensor network algorithm for quantum spin ladder systems

通过基于矩阵乘积态(MPS)的强关联电子量子自旋梯子格点系统的张量网络(TN)算法，摸索研究自旋梯子量子多体系统的弦序参量，探测系统的量子相变点，刻画系统的量子临界现象，获取系统的量子相图，这为我们提供了一个研究自旋梯子系统的量子多体物理性质强有力的工具和方法：在不知道系统是否缺乏Landau对称性破缺序或者系统是否存在相关的拓扑弦序的情况下，可以先得到系统的基态波函数，如果基态缺乏Landau对称性破缺序，或可以通过其它方式找出系统存在若干非局域的弦序参量，来完整地描述一些拓扑量子相变点，获得系统的量子相图，从而极大地丰富和发展了传统的Landau对称性破缺的相变理论。
Quantum spin ladder systems is systematically studied in terms of a tensor network (TN) algorithm, which is based on the tensor network representation of quantum many-body states as an adaptation of the matrix product states (MPS) to the geometry of translationally invariant infinite-size quantum spin ladders. Algorithm provides an effective method to generate the ground state wave function, which, on the other hand, allow us to compute the string order parameters, a universal marker to detect phase transitions in a quantum many-body system.