[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117778-en":3,"doc-seo-117778-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117778,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Classical and quantum machine learning applications in spintronics","This article demonstrates how classical and quantum machine learning can accelerate prediction in quantum transport and spintronics. Using a two-terminal device with a magnetic impurity, the work predicts highly non-linear conductance and the non-equilibrium spin response for arbitrary magnetic configurations. By mapping the quantum transport task to a classification problem, it achieves higher accuracy than conventional regression, especially beyond the linear response regime. The proposed quantum ML approach targets exponentially large configuration spaces. Results support designing nano devices and extending analysis to solid-state and molecular systems.","arXiv :2207 . 12837v2 [ cond-mat .mes-hall ] 27 Feb 2023  \nClassical and quantum machine learning applications in spintronics  \nKumar J. B. Ghosh􀀃  \nE. ON Digital Technology GmbH, 45131, Essen, Germany.  \nSumit Ghosh†  \nInstitute of Advance Simulations, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany and Institute of Physics, Johannes Gutenberg-University Mainz, 55128 Mainz, Germany  \nIn 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 thenon-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.  \nI. INTRODUCTION  \nIn recent years machine learning techniques [1] have become powerful tools in various research fields, for e.g., material science and chemistry [2–4], power and energy sector [5, 6], cyber security and anomaly detection[7, 8], drug discovery [9], etc. These techniques can be implemented on classical as well as quantum computers [10] which makes them even more powerful specially for problems which are unsolvable by any conventional means. There are extensive ongoing efforts on the application of quantum computing in the areas of machine learning [11– 13], finance [14], quantum chemistry [15, 16], drug design and molecular modeling [17], power systems [18, 19], metrology [20], to name a few applications. Quantumenabled methods are the next natural step of the AI studies to support faster computation and more accurate decision making, creating the interdisciplinary field of quantum artificial intelligence [21] .  \nRecently machine learning (ML) and quantum computing (QC) applications are gaining attention in the field of condensed matter physics [22–25] . Most of the studies so far are focused on the electronic properties [26–28] or transport properties [29, 30] . The application of ML has significantly reduced the computational requirement as well as time consumption for computationally demanding problems. In this paper we address another very active and promising brunch of condensed matter physics namely spintronics which is focused on manipulating spin degree of freedom and has been in the heart of modern computational device technology. Here we employ classical and quantum machine learning algorithm to predict two main observables in spintronics, namely non-equilibrium spin density generated by an applied electric field and the  \n􀀃 jb.ghosh@outlook.com † [s.ghosh@fz-juelich.de](s.ghosh@fz-juelich.de)  \ntransmission coefficient in a two terminal device configuration in presence of magnetic impurity. This configuration is the basis of any magnetic memory device where the non-equilibrium spin density provides the torque necessary for manipulating the magnetization [31, 32] . The theoretical evaluation of non-equilibrium spin density is done via non-equilibrium Green's function technique [33– 35] which is computationally quite demanding. Compared to that, prediction with trained learning algorithm is quite efficient [29, 30] and allows to study a large number of configurations. For a given system, the spintronic properties are usually dominated by a subset of parameters necessary to define the whole system. In this m","cbCaifiyxEwpyt1B","https://ap.wps.com/l/cbCaifiyxEwpyt1B","pdf",3337923,1,9,"English","en",105,"# Introduction\n## Machine learning and quantum computing background\n# Model and Method\n## Tight binding model and non-equilibrium Green's function approach\n# Results and Discussion\n## Classical and quantum ML outcomes\n# Conclusion","[{\"question\":\"What problem does the article address in spintronics?\",\"answer\":\"It addresses efficient prediction of non-linear conductance and non-equilibrium spin response in a two-terminal device with magnetic impurity, where calculations are computationally demanding.\"},{\"question\":\"How does the method improve accuracy compared with conventional regression?\",\"answer\":\"It discretizes the continuous outcomes and converts the nonlinear regression task into a classification problem, yielding higher accuracy across a broad energy range beyond linear response.\"},{\"question\":\"What role does quantum machine learning play in the proposed workflow?\",\"answer\":\"Quantum ML is presented as a tool that can handle significantly larger configuration spaces, including cases exponentially large for which classical algorithms become impractical.\"}]","Classical and quantum machine learning applications in spintronics | 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problem does the article address in spintronics?","Question",{"text":75,"@type":76},"It addresses efficient prediction of non-linear conductance and non-equilibrium spin response in a two-terminal device with magnetic impurity, where calculations are computationally demanding.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method improve accuracy compared with conventional regression?",{"text":80,"@type":76},"It discretizes the continuous outcomes and converts the nonlinear regression task into a classification problem, yielding higher accuracy across a broad energy range beyond linear response.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does quantum machine learning play in the proposed workflow?",{"text":84,"@type":76},"Quantum ML is presented as a tool that can handle significantly larger configuration spaces, including cases exponentially large for which classical algorithms become 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