Issue 2, 2023

Classical and quantum machine learning applications in spintronics

Abstract

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 impurities 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 nanodevices.

Graphical abstract: Classical and quantum machine learning applications in spintronics

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Article information

Article type
Paper
Submitted
20 sep 2022
Accepted
22 feb 2023
First published
01 mar 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 512-519

Classical and quantum machine learning applications in spintronics

K. J. B. Ghosh and S. Ghosh, Digital Discovery, 2023, 2, 512 DOI: 10.1039/D2DD00094F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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