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arXiv 2304.09504cond-mat.mes-hallcond-mat.dis-nn
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神经网络量子态描述的量子斯格明子的基态性质

Ground State Properties of Quantum Skyrmions described by Neural Network Quantum States

Ashish Joshi, Robert Peters, Thore Posske

AI总结:

本文利用神经网络量子态结合变分蒙特卡罗研究二维DMI海森堡模型中量子斯格明子的基态性质,发现中心自旋解纠缠可用于非破坏探测,并提出识别和缓解小DMI区域神经网络描述困难的方法。

摘要:

我们采用变分蒙特卡罗方法,以神经网络量子态作为变分拟设,研究了铁磁体中量子斯格明子的基态性质。我们研究的是存在Dzyaloshinskii-Moriya相互作用(DMI)的二维量子海森堡模型的基态。我们表明,在大范围参数下,尤其是在大DMI情况下,基态能够容纳一个量子斯格明子。这些量子斯格明子中的自旋处于弱纠缠状态,且纠缠程度随着DMI的减小而增加。我们还发现,中心自旋与晶格其余部分完全解纠缠,这为通过局域磁化测量以非破坏性方式探测此类斯格明子提供了一种方法。虽然神经网络非常适合探测具有大DMI的弱纠缠斯格明子,但在小DMI区域,由于近简并基态和强纠缠,神经网络难以描述斯格明子。在本文中,我们提出了一种识别该区域的方法以及一种缓解该问题的技术。最后,我们分析了神经网络的工作机制,并通过剪枝探索了其极限。我们的工作表明,神经网络量子态可以高效地用于描述远超精确对角化可处理规模的大系统的量子磁性。

英文摘要:

We investigate the ground state properties of quantum skyrmions in a ferromagnet using variational Monte Carlo with the neural network quantum state as variational ansatz. We study the ground states of a two-dimensional quantum Heisenberg model in the presence of the Dzyaloshinskii-Moriya interaction (DMI). We show that the ground state accommodates a quantum skyrmion for a large range of parameters, especially at large DMI. The spins in these quantum skyrmions are weakly entangled, and the entanglement increases with decreasing DMI. We also find that the central spin is completely disentangled from the rest of the lattice, establishing a non-destructive way of detecting this type of skyrmion by local magnetization measurements. While neural networks are well suited to detect weakly entangled skyrmions with large DMI, they struggle to describe skyrmions in the small DMI regime due to nearly degenerate ground states and strong entanglement. In this paper, we propose a method to identify this regime and a technique to alleviate the problem. Finally, we analyze the workings of the neural network and explore its limits by pruning. Our work shows that neural network quantum states can be efficiently used to describe the quantum magnetism of large systems exceeding the size manageable in exact diagonalization by far.

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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