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arXiv 1609.01263cond-mat.str-elcond-mat.stat-mechquant-ph
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量子晶格模型的级数展开热张量网络方法

Series-Expansion Thermal Tensor Network Approach for Quantum Lattice Models

Bin-Bin Chen, Yun-Jing Liu, Ziyu Chen, Wei Li

摘要:

在这项工作中,我们提出了一个系列膨胀热张量网络(SETTN)的方法,有效地模拟量子晶格模型。这种连续时间SETTN方法基于平衡密度算子$e^{-βH}$的数值精确泰勒级数展开(其中$H$为总哈密顿量,$β$为虚时间),因此是无Troter误差的。通过对XXZ自旋链和正方格子量子伊辛模型的模拟,我们发现不仅哈密顿量$H$,而且其幂$H^n$都可以有效地表示为矩阵乘积算符,这使我们能够高精度地计算有限温度下量子晶格模型的平衡和动力学性质.我们的SETTN方法提供了一种替代传统的Trotter-Suzuki重整化群(RG)的方法,并实现了前所未有的标准的热RG模拟的准确性和灵活性。

英文摘要:

In this work we propose a series-expansion thermal tensor network (SETTN) approach for efficient simulations of quantum lattice models. This continuous-time SETTN method is based on the numerically exact Taylor series expansion of equilibrium density operator $e^{-βH}$ (with $H$ the total Hamiltonian and $β$ the imaginary time), and is thus Trotter-error free. We discover, through simulating XXZ spin chain and square-lattice quantum Ising models, that not only the Hamiltonian $H$, but also its powers $H^n$, can be efficiently expressed as matrix product operators, which enables us to calculate with high precision the equilibrium and dynamical properties of quantum lattice models at finite temperatures. Our SETTN method provides an alternative to conventional Trotter-Suzuki renormalization group (RG) approaches, and achieves an unprecedented standard of thermal RG simulations in terms of accuracy and flexibility.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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