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arXiv 2311.03334cond-mat.str-elquant-ph
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抗磁性凯它夫模型中[111]磁场下的磁序涌现

Emergent magnetic order in the antiferromagnetic Kitaev model with a [111] field

Indiana University(印第安纳大学);University of British Columbia(不列颠哥伦比亚大学);University of Waterloo(滑铁卢大学)

Will Holdhusen, Daniel Huerga, Gerardo Ortiz

AI总结:

研究抗磁性凯它夫模型在[111]磁场下的量子相变,发现其经历两种中间相态,分别具有条纹和手性序,最终进入平凡部分极化相,与先前研究不同。

摘要:

凯它夫自旋液体作为凯它夫蜂窝模型的基态,是拓扑Z₂量子自旋液体的范例。在外部磁场下其命运成为当前研究热点,因为实验似乎揭示了所谓的凯它夫材料中的Z₂拓扑相,而理论研究预测了在平凡部分极化相出现前的中间量子相。本文采用分层平均场理论,一种基于保持相关对称性和短程量子相关性的簇方法,研究抗磁性凯它夫模型在[111]磁场下的量子相图。通过使用24个站点的簇,我们预测凯它夫自旋液体经历两种中间相态,分别具有条纹和手性序,最终进入平凡部分极化相,与先前研究不同。我们通过精确对角化和计算不同可观测量的标度,包括多体陈数和其他拓扑量,从而确立分层平均场理论作为研究拓扑量子自旋液体的方法。

英文摘要:

The Kitaev spin liquid, stabilized as the ground state of the Kitaev honeycomb model, is a paradigmatic example of a topological $\mathbb{Z}_2$ quantum spin liquid. The fate of the Kitaev spin liquid in presence of an external magnetic field is a topic of current interest due to experiments, which apparently unveil a $\mathbb{Z}_2$ topological phase in the so-called Kitaev materials, and theoretical studies predicting the emergence of an intermediate quantum phase of debated nature before the appearance of a trivial partially polarized phase. In this work, we employ hierarchical mean-field theory, an algebraic and numerical method based on the use of clusters preserving relevant symmetries and short-range quantum correlations, to investigate the quantum phase diagram of the antiferromagnetic Kitaev's model in a [111] field. By using clusters of 24 sites, we predict that the Kitaev spin liquid transits through two intermediate phases characterized by stripe and chiral order, respectively, before entering the trivial partially polarized phase, differing from previous studies. We assess our results by performing exact diagonalization and computing the scaling of different observables, including the many-body Chern number and other topological quantities, thus establishing hierarchical mean-field theory as a method to study topological quantum spin liquids.

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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