[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-327130-105":59,"doc-detail-327130-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","when-ai-meets-quantum-information-a-comprehensive-review","When AI meets quantum information - A comprehensive review","","Artificial intelligence and quantum information evolve in tandem, with AI increasingly supporting learning, designing, controlling, and verifying quantum systems, while quantum information provides fresh computational models, representations, and learning-theoretic questions for AI. This survey reviews the interface from both directions: AI methods for extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating workflows, and extending learning to sensing and networking; and quantum computation and quantum-inspired structures driving learning via speedups, expressivity, trainability, generalization, neural-network and tensor-network representations.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/when-ai-meets-quantum-information-a-comprehensive-review/327130/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/when-ai-meets-quantum-information-a-comprehensive-review/327130.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the survey cover about the relationship between AI and quantum information?","Question",{"text":113,"@type":114},"It reviews the interface in both directions, explaining how AI helps with learning and operation of quantum systems and how quantum/quantum-inspired methods influence AI learning.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which key tasks are highlighted for AI applied to quantum information?",{"text":118,"@type":114},"The survey organizes progress around information extraction from limited measurements, quantum algorithm training/discovery, noisy hardware stabilization, workflow automation, and extending learning-based methods to sensing and networking.",{"name":120,"@type":111,"acceptedAnswer":121},"What areas of learning impact are examined from the quantum information to AI direction?",{"text":122,"@type":114},"It discusses algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations shaped by quantum computation and quantum-inspired structures.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327130,1790468461,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","arXiv :2607 .00365v1 [ quant-ph] 1 Jul 2026  \nWhen AI meets quantum information: A comprehensive review  \nMin Chen, 1, ∗ Yu Gan, 1 Xin Jin, 1 Yuqing Li, 1 Junqi Wang, 1 Zeguan Wu, 1 Yunfei Wang,2, 3 Bingzhi Zhang,4 Priyam Srivastava,5 Tianlong Chen,6 Ankit Kulshrestha,7  \nYuan Liu,8, 9, 10 Juan Jos´e Mendoza-Arenas, 11, 12 Kaushik P. Seshadreesan,5 Sarvagya Upadhyay,7  \nXueyue Zhang, 13 Quntao Zhuang,4, 14 and Junyu Liu 1,†  \n1 Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA  \n2 Joint Center for Quantum Information and Computer Science,  \nNIST and University of Maryland, College Park, MD 20742, USA  \n3 Joint Quantum Institute, NIST and University of Maryland, College Park, MD 20742, USA  \n4 Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA  \n5 Department of Informatics and Networked Systems, School of Computing and Information,  \nUniversity of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA  \n6 Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, United States  \n7 Fujitsu Research of America, Santa Clara, CA 95054, USA  \n8 Department of Electrical and Computer Engineering,  \nNorth Carolina State University, Raleigh, North Carolina 27606, United States  \n9 Department of Computer Science, North Carolina State University, Raleigh, North Carolina 27606, United States  \n10 Department of Physics and Astronomy, North Carolina State University, Raleigh, North Carolina 27695, United States  \n11 Department of Mechanical Engineering and Materials Science,  \nUniversity of Pittsburgh, Pittsburgh, PA 15261, USA  \n12 Department of Physics and Astronomy, University of Pittsburgh, PA 15261, USA  \n13 Department of Applied Physics and Applied Mathematics,  \nColumbia University, New York, NY 10027, USA  \n14 Department of Physics and Astronomy, University of Southern California, Los Angeles, CA, USA  \n(Dated: July 2, 2026)  \nArtificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QIfor AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum–classical systems.  \nCONTENTS  \nI. Introduction 2  \nII. Preliminaries 3  \nA. Quantum States, Measurements, and Variational Models 3  \nB. AI and ML Preliminaries 4  \nC. Computational Access Models for Quantum Methods in AI 6  \nIII. Artificial Intelligence for Quantum Information 6  \nA. Statistically Learning Quantum Systems 6  \nB. AI-Theoretic Perspectives on Quantum  \nAlgorithms 11  \nC. AI for Quantum Algorithm Discovery 17  \n∗ [mic380@pitt.edu](mic380@pitt.edu)[ ](mic380@pitt.edu)† [junyuliu@pitt.edu](junyuliu@pitt.edu)  \nD. Learning, Correction, and Control of Noisy  \nQuantum Systems 18  \nE. Autonomous Quantum Workflow Orchestration 20  \nF. AI for quantum sensing 24  \nG. AI for quantum networking 26  \nIV. Quantum Computing and Quantum-Inspired Methods  \nfor Artificial Intelligence 28  \nA. Quantum Algorithmic Speedups for Classical ML  \nTasks 28  \nB. Quantum Comput","cbCaivoqrACOz23J","https://ap.wps.com/l/cbCaivoqrACOz23J","pdf",1599122,62,"English","# Contents\n## Introduction\n## Preliminaries\n## Artificial Intelligence for Quantum Information\n## Quantum Computing and Quantum-Inspired Methods for Artificial Intelligence\n## Cross-Cutting Challenges and Outlook\n## Conclusion","[{\"question\":\"What does the survey cover about the relationship between AI and quantum information?\",\"answer\":\"It reviews the interface in both directions, explaining how AI helps with learning and operation of quantum systems and how quantum/quantum-inspired methods influence AI learning.\"},{\"question\":\"Which key tasks are highlighted for AI applied to quantum information?\",\"answer\":\"The survey organizes progress around information extraction from limited measurements, quantum algorithm training/discovery, noisy hardware stabilization, workflow automation, and extending learning-based methods to sensing and networking.\"},{\"question\":\"What areas of learning impact are examined from the quantum information to AI direction?\",\"answer\":\"It discusses algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations shaped by quantum computation and quantum-inspired structures.\"}]","When AI meets quantum information - A comprehensive review | PDF",1789976495,156]