Real-time impurity solver using Grassmann time-evolving matrix product operators

被引:6
作者
Chen, Ruofan [1 ,2 ]
Xu, Xiansong [1 ,2 ,3 ]
Guo, Chu [4 ,5 ]
机构
[1] Sichuan Normal Univ, Coll Phys & Elect Engn, Chengdu 610068, Peoples R China
[2] Sichuan Normal Univ, Ctr Computat Sci, Chengdu 610068, Peoples R China
[3] Singapore Univ Technol & Design, Sci & Math Cluster, 8 Somapah Rd, Singapore 487372, Singapore
[4] Hunan Normal Univ, Minist Educ, Dept Phys, Key Lab Low Dimens Quantum Struct & Quantum Contro, Changsha 410081, Peoples R China
[5] Hunan Normal Univ, Synerget Innovat Ctr Quantum Effects & Applicat, Changsha 410081, Peoples R China
基金
中国国家自然科学基金;
关键词
RENORMALIZATION-GROUP; QUANTUM; DYNAMICS; SYSTEMS; TRANSITION; FERMIONS; MODEL;
D O I
10.1103/PhysRevB.109.165113
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
An emergent and promising tensor-network-based impurity solver is to represent the Feynman-Vernon influence functional as a matrix product state, where the bath is integrated out analytically. Here we present an approach to calculate the equilibrium impurity spectral function based on the recently proposed Grassmann time-evolving matrix product operators method. The central idea is to perform a quench from a separable impurity-bath initial state as in the nonequilibrium scenario. The retarded Green's function G(t + t0, t' + t0) is then calculated after an equilibration time t0 such that the impurity and bath are approximately in thermal equilibrium. There are two major advantages of this method. First, since we focus on real-time dynamics, we do not need to perform the numerically ill-posed analytic continuation as in imaginary- time evolution-based methods. Second, the required bond dimension of the matrix product state in real-time calculations is observed to be much smaller than that in imaginary-time calculations, leading to a significant improvement in numerical efficiency. The accuracy of this method is demonstrated using the single-orbital Anderson impurity model and benchmarked against the continuous-time quantum Monte Carlo method.
引用
收藏
页数:10
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