Classical and quantum machine learning applications in spintronics
- URL: http://arxiv.org/abs/2207.12837v1
- Date: Tue, 26 Jul 2022 12:10:49 GMT
- Title: Classical and quantum machine learning applications in spintronics
- Authors: Kumar Ghosh and Sumit Ghosh
- Abstract summary: We show how machine learning algorithms can predict the highly non-linear nature of conductance.
We describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
We finally describe the applicability of quantum machine learning which has the
capability to handle a significantly large configuration space. Our approach is
also applicable for molecular systems. These outcomes are crucial in predicting
the behaviour of large scale systems where a quantum mechanical calculation is
computationally challenging and therefore would play a crucial role in
designing nano devices.
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