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arXiv 1903.08482cond-mat.str-elcond-mat.mes-hall
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用机器学习研究超快量子磁性

Investigating ultrafast quantum magnetism with machine learning

G. Fabiani, J. H. Mentink

摘要:

研究了最近提出的量子多体态的限制玻尔兹曼机(RBM)表示对研究二维正方晶格海森堡模型的静态性质和量子自旋动力学的有效性.对于静态性能,我们发现密切的协议与数值精确的量子Monte Carlo结果的临界极限。对于动力学和小系统,我们发现很好的协议与精确对角化,而系统高达N=256自旋密切的一致性与相互作用的自旋波理论。在所有情况下,准确度随着网络参数的数量而快速收敛,从而可以访问比以前可行的更大的系统。这表明研究大尺度自旋系统的量子多体动力学具有很大的潜力,它与描述磁性材料的强非平衡态有关。

英文摘要:

We investigate the efficiency of the recently proposed Restricted Boltzmann Machine (RBM) representation of quantum many-body states to study both the static properties and quantum spin dynamics in the two-dimensional Heisenberg model on a square lattice. For static properties we find close agreement with numerically exact Quantum Monte Carlo results in the thermodynamical limit. For dynamics and small systems, we find excellent agreement with exact diagonalization, while for systems up to N=256 spins close consistency with interacting spin-wave theory is obtained. In all cases the accuracy converges fast with the number of network parameters, giving access to much bigger systems than feasible before. This suggests great potential to investigate the quantum many-body dynamics of large scale spin systems relevant for the description of magnetic materials strongly out of equilibrium.

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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