Classical and quantum machine learning applications in spintronics
arXiv:2207.12837 · doi:10.1039/D2DD00094F
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. 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.
arXiv comment9 pages, 8 figures
References in corpus (24)
- Quantum Machine Learning
- Quantum-enhanced measurements: beating the standard quantum limit
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- Quantum Circuit Learning
- Quantum Chemistry in the Age of Quantum Computing
- An introduction to quantum machine learning
- Kwant: a software package for quantum transport
- Quantum random access memory
- Quantum computing with defects
- Towards Quantum Chemistry on a Quantum Computer
- Quantum Risk Analysis
- Simulating quantum many-body dynamics on a current digital quantum computer
- Experimental Realization of Quantum Artificial Intelligence
- Perspective on integrating machine learning into computational chemistry and materials science
- Can Non-Equilibrium Spin Hall Accumulation be Induced in Ballistic Nanostructures?
- A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
- Experimental realization of quantum algorithm for solving linear systems of equations
- Spin-orbit torque in 3D topological insulator-ferromagnet heterostructure: crossover between bulk and surface transport
- Modeling Electronic Quantum Transport with Machine Learning
- Hybrid Classical-Quantum Autoencoder for Anomaly Detection
- Spin Orbit Torque in two dimensional Antiferromagnetic Topological Insulators
- Experimental Quantum Computing to Solve Network DC Power Flow Problem
- Non-equilibrium spin density and spin-orbit torque in three dimensional topological insulators - antiferromagnet heterostructure