[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117745-en":3,"doc-seo-117745-105":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},117745,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Artificial Intelligence and Machine Learning for Quantum Technologies","Recent advances in machine learning are reshaping scientific and technological research, and this perspective article explains how quantum technologies benefit from that shift. It presents illustrative examples showing how AI and ML are used to analyze quantum measurements, estimate device parameters, design new quantum experimental setups, protocols, and feedback strategies, and improve quantum computing, communication, and simulation. The review highlights open challenges and future directions, and concludes with forward-looking ideas for the coming decade.","Arti􀀌cial Intelligence and Machine Learning for Quantum Technologies  \narXiv :2208 .03836v1 [ quant-ph] 7 Aug 2022  \nMario Krenn, 1, 􀀃 Jonas Landgraf, 1, 2 Thomas Foesel, 1, 2 and Florian Marquardt 1, 2  \n1 Max Planck Institute for the Science of Light, Erlangen, Germany.  \n2 Department of Physics, Friedrich-Alexander Universit􀁿at Erlangen-N􀁿urnberg, Germany.  \n(Dated: August 9, 2022)  \nIn recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology signi􀀌cantly. In the present perspective article, we explore how quantum technologies are bene􀀌ting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of arti􀀌cial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.  \nCONTENTS  \nI. Introduction 1  \nII. Basic Techniques of Machine Learning and Arti􀀌cial Intelligence 2  \nA. Evolutionary Algorithms 2  \nB. Neural Networks: Structure 2  \nC. Neural Networks: Training 3  \nD. Unsupervised Learning 4  \nE. Reinforcement Learning 4  \nF. Automatic di􀀋erentiation and gradient-based optimization 5  \nG. How to get started 5  \nIII. Applications of Machine Learning for Quantum Technologies 5  \nA. Measurement data analysis and quantum state representation 5  \nB. Parameter estimation: learning the properties of quantum systems 8  \nC. Discovering strategies for hardware-level quantum control 10  \nD. Discovering quantum experiments, protocols, and circuits 12  \nE. Quantum Error Correction 16  \nIV. Outlook 17  \nV. Acknowledgments 18  \nReferences 18  \nI. INTRODUCTION  \nThe 􀀌elds of machine learning [1{3] and quantum technologies [4{7] have a lot in common: Both started out  \n􀀃 [ML4qtech@mpl.mpg.de](ML4qtech@mpl.mpg.de)  \nwith an amazing vision of applications (in the 1950sand 1980s, respectively), went through a series of challenges, and are currently extremely hot research topics. Of these two, machine learning has 􀀌rmly taken hold beyond academia and beyond prototypes, triggering a revolution in technological applications during the past decade. This perspective article will be concerned with shining a spotlight on how techniques of classical machine learning (ML) and arti􀀌cial intelligence (AI) hold great promise for improving quantum technologies in the future. A wide range of ideas have been developed at this interface between the two 􀀌elds during the past 􀀌ve years, see Fig. 1. Whether one tries to understand a quantum state through measurements, discover optimal feedback strategies or quantum error correction protocols, or design new quantum experiments, machine learning can yield e􀀎cient solutions, optimized performance and, in the best cases, even new insights.  \nWith the present review, we aim to take physicists with a background in quantum technologies on a tour of this rapidly growing area at the interface to classical machine learning. The readers are not expected to have a background in machine learning and will get a state-of-the-art view into how machine learning techniques are applied to quantum physics. We should state right away that in this perspective article we tried to achieve our goal by focusing in each application domain on some selected illustrative examples. Our selection is necessarily subjective. We thus make no claim as to providing a comprehensive list of the literature and apologize to anyone who misses some favorite work.  \nWe hope that after seeing the examples discussed in our review, the reader will appreciate how useful machine learning techniques could be for quantum technologies. At the same time, we also want to make the reader awa","cbCaibUocDsdN5bq","https://ap.wps.com/l/cbCaibUocDsdN5bq","pdf",1603247,1,23,"English","en",105,"# Introduction\n# Basic Techniques of Machine Learning and Artificial Intelligence\n## Evolutionary Algorithms\n## Neural Networks: Structure\n## Neural Networks: Training\n## Unsupervised Learning\n## Reinforcement Learning\n## Automatic differentiation and gradient-based optimization\n## How to get started\n# Applications of Machine Learning for Quantum Technologies\n## Measurement data analysis and quantum state representation\n## Parameter estimation\n## Discovering strategies for hardware-level quantum control\n## Discovering quantum experiments, protocols, and circuits\n## Quantum Error Correction\n# Outlook\n# Acknowledgments\n# References","[{\"question\":\"How does machine learning improve quantum technologies?\",\"answer\":\"The article describes how ML/AI can analyze quantum measurements, estimate quantum device parameters, and discover improved experimental setups, protocols, and feedback strategies, leading to better performance in quantum computing, communication, and simulation.\"},{\"question\":\"What machine learning techniques are introduced as core building blocks?\",\"answer\":\"It covers evolutionary algorithms, neural network structure and training, unsupervised learning, reinforcement learning, and automatic differentiation with gradient-based optimization, along with guidance on how to get started.\"},{\"question\":\"What are the main application areas for ML in quantum technologies?\",\"answer\":\"Key applications include measurement data analysis and state representation, parameter estimation, hardware-level quantum control strategies, discovering quantum experiments/protocols/circuits, and approaches related to quantum error correction.\"}]","Artificial Intelligence and Machine Learning for Quantum Technologies | 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does machine learning improve quantum technologies?","Question",{"text":75,"@type":76},"The article describes how ML/AI can analyze quantum measurements, estimate quantum device parameters, and discover improved experimental setups, protocols, and feedback strategies, leading to better performance in quantum computing, communication, and simulation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning techniques are introduced as core building blocks?",{"text":80,"@type":76},"It covers evolutionary algorithms, neural network structure and training, unsupervised learning, reinforcement learning, and automatic differentiation with gradient-based optimization, along with guidance on how to get started.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main application areas for ML in quantum technologies?",{"text":84,"@type":76},"Key applications include measurement data analysis and state representation, parameter estimation, hardware-level quantum control 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