[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31648":3,"doc-seo-31648":27},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31648,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Quantum Machine Learning (De Gruyter Frontiers in Computational Intelligence Volume 6)","Quantum Machine Learning (Volume 6) is a scholarly edited volume presenting foundational and advanced topics at the intersection of quantum computing and machine learning. It includes a structured set of chapters covering quantum machine learning introductions, topographic representation, quantum optimization methods, and transitions from classical to quantum approaches. The contents also feature quantum-inspired automatic clustering techniques, including a comparative study of genetic algorithm and bat algorithm, followed by concluding remarks and an index.","cbCaitQo1QDyGwS5","https://ap.wps.com/l/cbCaitQo1QDyGwS5","pdf",1283389,1,133,"English","en","# Contents\n## Preface\n## List of Contributors\n## 1 Introduction to quantum machine learning\n## 2 Topographic representation for quantum machine learning\n## 3 Quantum optimization for machine learning\n## 4 From classical to quantum machine learning\n## 5 Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm\n## 6 Conclusion\n## Index","[{\"question\":\"What main topics does the volume cover in quantum machine learning?\",\"answer\":\"The volume covers an introduction to quantum machine learning, topographic representation, quantum optimization, and methods bridging classical and quantum machine learning. It also includes quantum-inspired clustering algorithms and a final conclusion and index.\"},{\"question\":\"Is there material comparing different clustering approaches?\",\"answer\":\"Yes. Chapter 5 presents quantum inspired automatic clustering algorithms and includes a comparative study of Genetic algorithm and Bat algorithm.\"},{\"question\":\"Who is responsible for organizing the edited volume?\",\"answer\":\"The document lists multiple editors: Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, and Susanta Chakraborti.\"}]",1779829339,335,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"quantum-machine-learning-de-gruyter-frontiers-in-computational-intelligence-volume-6","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/quantum-machine-learning-de-gruyter-frontiers-in-computational-intelligence-volume-6/31648/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-26",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What main topics does the volume cover in quantum machine learning?","Question",{"text":73,"@type":74},"The volume covers an introduction to quantum machine learning, topographic representation, quantum optimization, and methods bridging classical and quantum machine learning. It also includes quantum-inspired clustering algorithms and a final conclusion and index.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Is there material comparing different clustering approaches?",{"text":78,"@type":74},"Yes. Chapter 5 presents quantum inspired automatic clustering algorithms and includes a comparative study of Genetic algorithm and Bat algorithm.",{"name":80,"@type":71,"acceptedAnswer":81},"Who is responsible for organizing the edited volume?",{"text":82,"@type":74},"The document lists multiple editors: Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, and Susanta Chakraborti.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]