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arXiv 2103.07236cond-mat.str-elcond-mat.dis-nn
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格子结构正方晶格上自旋1/2量子反铁磁体的机器学习量子临界性

Machine learning quantum criticality in the spin-1/2 quantum antiferromagnets on the square lattice with plaquette structure

Tanja Duric

摘要:

机器学习算法自动分类物质的不同相并检测量子相变而无需像传统方法那样通过诸如局部序参量或拓扑不变量的各种量来表征相的能力将物质的机器学习相定义为凝聚态和统计物理学中的新的研究前沿和基础研究工具。结合强化和监督机器学习技术,研究了具有额外元格结构的自旋1/2正方格子J1-J2模型的量子临界性。在我们的计算中,基态自旋自旋相关矩阵的几个系统尺寸首先找到限制玻尔兹曼机的变分蒙特卡罗方法,相当于强化学习,然后作为训练数据的卷积神经网络的监督机器学习算法相分类。该模型表现出从顺磁元格共振价键态到反铁磁态的量子相变,由于其与铜氧化物超导体的密切联系以及在冷原子实验中实现的可能性,一直是人们感兴趣的话题。我们认为这两个受挫和未受挫的制度,并比较我们的结果与以前得到的结果与其他方法。我们发现,我们的结果与耦合集团和实空间重整化群方法得到的结果在阻挫和非阻挫区都是很好的一致。量子蒙特卡罗和有限尺寸标度的结果,然而,从我们的结果之间的元胞间耦合强度的临界值略有不同,虽然结果仍然是在合理的协议。

英文摘要:

The power of machine learning algorithms to automatically classify different phases of matter and detect quantum phase transitions without necessity to characterize phases by various quantities like local order parameters or topological invariants as in conventional approaches defined machine learning phases of matter as a new research frontier and basic research tool in condensed matter and statistical physics. We study quantum criticality in the spin-1/2 square-lattice J1-J2 model with additional plaquette structure by combination of reinforcement and supervised machine learning techniques. In our calculations the ground-state spin-spin correlation matrices for several system sizes are first found by restricted Boltzmann machine based variational Monte Carlo method, equivalent to reinforcement learning, and then used as a training data for convolutional neural network based supervised machine learning algorithm for phases classification. The model exhibits a quantum phase transition from paramagnetic plaquette resonating valence bond state to an antiferromagnetic state and has been a topic of great interest because of its close connection to cuprate superconductors and possibility of realization in cold atoms experiments. We consider both frustrated and unfrustrated regimes and compare our results with the results obtained previously with other methods. We find that our results are in good agreement with the results obtained by coupled cluster and real space renormalization group methods for both frustrated and unfrustrated regimes. The quantum Monte Carlo and finite-size scaling result for the unfrustrated case however slightly differs from our result for the critical value of the inter-plaquette coupling strength, although the results are still in reasonable agreement.

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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