论文标题
经典和量子机器学习应用程序
Classical and quantum machine learning applications in spintronics
论文作者
论文摘要
在本文中，我们演示了经典和量子机学习在量子传输和旋转基质中的应用。借助具有磁性杂质的两端设备，我们显示了机器学习算法如何预测电导的高度非线性性质以及任何随机磁性构型的非平衡自旋响应函数。通过将此量子机械问题映射到分类问题上，与使用常规回归方法获得的预测相比，我们能够获得更高的精度。我们最终描述了量子机学习的适用性，该学习能够处理明显的配置空间。我们的方法适用于固态设备以及分子系统。这些结果对于预测量子机械计算在计算上具有挑战性的大规模系统的行为至关重要，因此在设计纳米设备中起着至关重要的作用。
In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurity we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. By mapping this quantum mechanical problem onto a classification problem, we are able to obtain much higher accuracy beyond the linear response regime compared to the prediction obtained with conventional regression methods. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is applicable for solid state devices as well as for molecular systems. These outcomes are crucial in predicting the behavior of large-scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nano devices.
