[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117736-en":3,"doc-seo-117736-105":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},117736,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Modern applications of machine learning in quantum sciences","Lecture notes provide a structured, comprehensive introduction to recent advances in applying machine learning to quantum sciences. Coverage spans deep learning and kernel methods across supervised, unsupervised, and reinforcement learning, with applications including phase classification, representation of many-body quantum states, quantum feedback control, and quantum-circuit optimization. The notes further discuss specialized directions such as differentiable programming, generative models, statistical approaches to machine learning, and quantum machine learning, connecting methods to quantum tasks and open problems.","Modern applications of machine learning in quantum sciences  \nAnna Dawid1,2?, Julian Arnold3†, Borja Requena2†, Alexander Gresch4†, Marcin Płodzie2 , Kaelan Donatella5 , Kim Nicoli6,7 , Paolo Stornati2 , Rouven Koch8 , Miriam Büttner9 ,  \nRobert Okuła10 , Gorka Muñoz–Gil11 , Rodrigo A. Vargas–Hernández12,13 , Alba Cervera-Lierta14 , Juan Carrasquilla13 , Vedran Dunjko15 , Marylou Gabrié16,17 , Patrick Huembeli18,19 , Evert van Nieuwenburg20 , Filippo Vicentini18 , Lei Wang21,22 , Sebastian J. Wetzel23 , Giuseppe Carleo18 , Eliška Greplová24 , Roman Krems25 , Florian Marquardt26,27 , Michał Tomza1 , Maciej Lewenstein2,28 and Alexandre Dauphin2?  \n1 Faculty of Physics, University of Warsaw, Poland  \n2 ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology,  \n08860 Castelldefels (Barcelona), Spain  \n3 Department of Physics, University of Basel, Switzerland  \n4 Quantum Technology Research Group, Heinrich-Heine-Universität Düsseldorf, Germany  \n5 Université de Paris, CNRS, Laboratoire Matériaux et Phénomènes Quantiques, France  \n6 Machine Learning Group, Technische Universität Berlin, Germany  \n7 BIFOLD, Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany  \n8 Department of Applied Physics, Aalto University, Espoo, Finland  \n9 Institute of Physics, Albert-Ludwig University of Freiburg, Germany  \n10 International Centre for Theory of Quantum Technologies, University of Gdask, Poland  \n11 Institute for Theoretical Physics, University of Innsbruck, Austria  \n12 Department of Chemistry, University of Toronto, Canada  \n13 Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, Canada  \n14 Barcelona Supercomputing Center, Spain  \n15 LIACS, Leiden University, The Netherlands  \n16 CMAP, École Polytechnique, France  \n18 Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland  \n19 Menten AI, Inc., Palo Alto, California, United States of America  \n20 Niels Bohr International Academy, Copenhagen, Denmark  \n21 Beijing National Lab for Condensed Matter Physics  \nand Institute of Physics, Chinese Academy of Sciences, Beijing, China  \n22 Songshan Lake Materials Laboratory, Dongguan, China  \n23 Perimeter Institute for Theoretical Physics, Waterloo, Canada  \n24 Kavli Institute of Nanoscience, Delft University of Technology, NL-2600 GA Delft, The Netherlands  \n25 Department of Chemistry, University of British Columbia, Vancouver, Canada  \n26 Max Planck Institute for the Science of Light, Erlangen, Germany  \n27 Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, Germany  \n28 ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain † These authors contributed equally.  \n? [Anna.Dawid@fuw.edu.pl](Anna.Dawid@fuw.edu.pl) , [Alexandre.Dauphin@icfo.eu](Alexandre.Dauphin@icfo.eu)  \nApril 8, 2022  \nAbstract  \nIn these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.  \nIn memory of Peter Wittek  \nContents  \n1 Introduction 6  \n1.1 How to make computers learn? 6  \n1.2 Historical view on learning machines 7  \n1.3 Learning machines viewed by a statistical physics 9  \n1.4 Examples of tasks 11  \n1.5 Types of learning 12  \n1.6 What are these Lecture Notes about 14  \n2 Basics of machine learning 18  \n2.1 Learning as an optimization problem 18  \n2.2 Generalization and regularization 22  \n2.3 Probabilistic view on machine learning 25  \n2.4 Machine learning models 28  \n2.4.1 Linear (ridge) regression 29  \n2.4.2 Logistic regressi","cbCaicPZ0d3Fd0yv","https://ap.wps.com/l/cbCaicPZ0d3Fd0yv","pdf",18769372,1,268,"English","en",105,"# 1 Introduction\n## 1.1 How to make computers learn?\n## 1.2 Historical view on learning machines\n## 1.3 Learning machines viewed by a statistical physics\n## 1.4 Examples of tasks\n## 1.5 Types of learning\n## 1.6 What are these Lecture Notes about\n# 2 Basics of machine learning\n## 2.1 Learning as an optimization problem\n## 2.2 Generalization and regularization\n## 2.3 Probabilistic view on machine learning\n## 2.4 Machine learning models\n## 2.4.1 Linear (ridge) regression\n## 2.4.2 Logistic regression\n## 2.4.3 Support vector machines\n## 2.4.4 Neural networks\n## 2.4.5 Autoencoders\n## 2.4.6 Autoregressive neural networks\n# 3 Phase classification\n## 3.1 Prototypical physical systems for the study of phases of matter\n## 3.2 Unsupervised phase classification without neural networks\n## 3.3 Supervised phase classification with neural networks\n## 3.4 Unsupervised phase classification with neural networks\n## 3.5 Interpretability of machine learning models\n## 3.6 Outlook and open problems","[{\"question\":\"What learning paradigms are covered in these lecture notes?\",\"answer\":\"The notes cover supervised, unsupervised, and reinforcement learning algorithms, and relate them to multiple quantum-science tasks.\"},{\"question\":\"Which quantum-science applications are specifically discussed?\",\"answer\":\"They discuss phase classification, representation of many-body quantum states, quantum feedback control, and optimization of quantum circuits.\"},{\"question\":\"What specialized topics beyond standard ML are included?\",\"answer\":\"The notes include differentiable programming, generative models, statistical approaches to machine learning, and quantum machine learning.\"}]","Modern applications of machine learning in quantum sciences | 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learning paradigms are covered in these lecture notes?","Question",{"text":75,"@type":76},"The notes cover supervised, unsupervised, and reinforcement learning algorithms, and relate them to multiple quantum-science tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which quantum-science applications are specifically discussed?",{"text":80,"@type":76},"They discuss phase classification, representation of many-body quantum states, quantum feedback control, and optimization of quantum circuits.",{"name":82,"@type":73,"acceptedAnswer":83},"What specialized topics beyond standard ML are included?",{"text":84,"@type":76},"The notes include differentiable programming, generative models, statistical approaches to machine learning, and quantum machine 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