[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31568":3,"doc-seo-31568":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},31568,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","5G Radio Link Failure Prediction using Quantum Machine Learning","5G wireless networks promise ultra-high-speed, low-latency, high-reliability service, yet radio link failures (RLFs) can undermine performance and user experience. A quantum machine learning framework is presented to forecast RLFs using a quantum long short-term memory (QLSTM) model with quantum circuit embeddings, including Angle embedding, amplitude embedding, and QAOA embedding. The method further integrates quantum circuit outputs with classical LSTM to assess improvements. Evaluation on a real-world dataset shows higher accuracy and lower loss than classical machine learning approaches.","cbCaielHielY2okB","https://ap.wps.com/l/cbCaielHielY2okB","pdf",655515,1,5,"English","en","# Introduction\n# Data Source","[{\"question\":\"What dataset is used and what results are reported?\",\"answer\":\"The dataset is collected from “Turkcell mobile networks” in Turkey as part of the ITU “AI for 5G Challenge,” covering data from 2018 to 2020. Experimental results indicate the proposed QML approach outperforms classical machine learning methods in accuracy and loss.\"}]",1779742875,13,{"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":76,"head_meta":78,"extra_data":80,"updated_unix":25},105,"5g-radio-link-failure-prediction-using-quantum-machine-learning","",{"@graph":34,"@context":75},[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/5g-radio-link-failure-prediction-using-quantum-machine-learning/31568/",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","2026-05-25",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69],{"name":70,"@type":71,"acceptedAnswer":72},"What dataset is used and what results are reported?","Question",{"text":73,"@type":74},"The dataset is collected from “Turkcell mobile networks” in Turkey as part of the ITU “AI for 5G Challenge,” covering data from 2018 to 2020. Experimental results indicate the proposed QML approach outperforms classical machine learning methods in accuracy and loss.","Answer","https://schema.org",{"og:url":50,"og:type":77,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":79,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]