估计经典与量子斯格明子态的模式
Estimating Patterns of Classical and Quantum Skyrmion States
AI总结：
本文综述了利用机器学习算法表征磁性斯格明子的最新进展，指出经典自旋系统可通过少量磁化快照实现准确的相分类与定量描述，并探讨了在近期量子计算机上模拟量子斯格明子态的方法。
摘要：
在这篇综述中，我们讨论了关于磁性斯格明子机器学习算法开发的最新结果。磁性斯格明子是源于Dzyaloshinskii-Moriya相互作用（在铁磁体中与Heisenberg各向同性交换相竞争）的拓扑保护磁织构。我们表明，对于经典自旋系统，存在一整套机器学习方法，允许基于少量磁化快照进行准确的相分类和定量描述。反过来，量子斯格明子的研究是一个较少探索的问题，因为用经典超级计算机模拟此类波函数存在根本限制。人们需要找到在近期量子计算机上模拟量子斯格明子的方法。在这方面，我们讨论了实现一种估计经典对象结构复杂度的方法，用于基于从投影测量获得的有限数量的比特串来表征量子斯格明子态。
英文摘要：
In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protected magnetic textures originated from the Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange in ferromagnets. We show that for classical spin systems there is a whole pool of machine approaches allowing their accurate phase classification and quantitative description on the basis of few magnetization snapshots. In turn, investigation of the quantum skyrmions is a less explored issue, since there are fundamental limitations on the simulation of such wave functions with classical supercomputers. One needs to find the ways to imitate quantum skyrmions on near-term quantum computers. In this respect, we discuss implementation of the method for estimating structural complexity of classical objects for characterization of the quantum skyrmion state on the basis of limited number of bitstrings obtained from the projective measurements.
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。
