[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120684-en":3,"doc-seo-120684-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120684,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","SEQUENT - Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training","Applying quantum computing to machine learning attracts attention, yet real-world high-dimensional tasks cannot be solved using purely quantum hardware. Hybrid approaches combine classical and quantum learning, where transfer learning has shown effectiveness in hybrid image classification. A key challenge is tracing how the chosen circuit architecture and parameterization influence performance, especially because current training methods update classical and quantum components concurrently. SEQUENT introduces sequential quantum enhanced training that enables separable assessment, provides formal evidence of limitations in existing methods, and reports preliminary proof-of-concept experiments.","arXiv :2301 .02601v2 [ quant-ph] 26 Apr 2023  \nSEQUENT: Towards Traceable Quantum Machine Learning using  \nSequential Quantum Enhanced Training 􀀃  \nPhilipp Altmann 1 , Leo S¨unkel 1 , Jonas Stein 1 , Tobias M¨uller2 , Christoph Roch 1 and Claudia Linnhoff-Popien 1  \n1LMU Munich  \n2 SAP SE, Walldorf, Germany  \nphilipp.altmann@ifi.[lmu.de](lmu.de)  \nKeywords: Quantum Machine Learning, Transfer Learning, Supervised Learning, Hybrid Quantum Computing.  \nAbstract: Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigmshave been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.  \n1 INTRODUCTION  \nWith classical computation evolving towards performance saturation, new computing paradigms like quantum computing arise, promising superior performance in complex problem domains. However, current architectures merely reach numbers of 100 quantum bits (qubits), prone to noise, and classical computers run out of resources simulating similar sized systems (Preskill, 2018) . Thus, most real world applications are not yet feasible solely relying on quantum compute. Especially in the field of machine learning, where parameter spaces sized upwards of 50 million are required for tasks like image classification, the resources of current quantum hardware or simulators is not yet sufficient for pure quantum approaches  \n*Citation: Altmann, P. ; S¨unkel, L. ; Stein, J. ; M¨uller, T.; Roch, C. and Linnhoff-Popien, C. (2023) . SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, pages 744-751 . DOI: 10.5220/0011772400003393  \n(He et al., 2016) . Therefore, hybrid approaches have been proposed, where the power of both classical and quantum computation are united for improved results (Bergholm et al., 2018) . By this, it is possible to leverage the advantages of quantum computing for tasks with parameter spaces that cannot be computed solely by quantum computers due to hardware and simulation limitations. Within those hybrid algorithms the quantum part is, analogue to the classical deep neural networks (DNNs), represented by so called variational quantum circuits (VQCs), which are parameterized and can be trained in a supervised manner using labeled data (Cerezo et al., 2021) . For hybrid machine learning, we will from hereon refer to VQCs  \nas quantum parts and to DNNs as classical parts.  \nTo solve large-scale real-world tasks, like imageclassification, the concept of transfer learning has been applied for training such hybrid models (Girshick et al., 2014; Pan and Yang, 2010) . Given a complex model, with high-dimensional input-and parameter spaces, the term transfe","cbCailgjgSh1Bvha","https://ap.wps.com/l/cbCailgjgSh1Bvha","pdf",765433,1,"English","en",105,"# Introduction\n## Motivation for hybrid quantum machine learning\n## Variational quantum circuits and training paradigm\n## Limitations of existing concurrent training\n## Proposed approach: Sequential Quantum Enhanced Training (SEQUENT)","[{\"question\":\"Why do current approaches struggle with traceability in hybrid quantum machine learning?\",\"answer\":\"They often train classical and quantum components concurrently, making it difficult to strictly separate how each part contributes to the final model accuracy.\"},{\"question\":\"What is SEQUENT designed to improve?\",\"answer\":\"SEQUENT improves the architecture and training process so the impacts of classical and quantum parts become separably assessable for hybrid quantum transfer learning.\"},{\"question\":\"How does the paper justify the need for separable assessment?\",\"answer\":\"The authors argue that without traceability, one cannot determine whether a chosen quantum circuit genuinely benefits classification or whether any inaccuracies are merely compensated by classical layers.\"}]","SEQUENT - Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training | PDF",1785731468,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"sequent-towards-traceable-quantum-machine-learning-using-sequential-quantum-enhanced-training","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/sequent-towards-traceable-quantum-machine-learning-using-sequential-quantum-enhanced-training/120684/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do current approaches struggle with traceability in hybrid quantum machine learning?","Question",{"text":74,"@type":75},"They often train classical and quantum components concurrently, making it difficult to strictly separate how each part contributes to the final model accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is SEQUENT designed to improve?",{"text":79,"@type":75},"SEQUENT improves the architecture and training process so the impacts of classical and quantum parts become separably assessable for hybrid quantum transfer learning.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the paper justify the need for separable assessment?",{"text":83,"@type":75},"The authors argue that without traceability, one cannot determine whether a chosen quantum circuit genuinely benefits classification or whether any inaccuracies are merely compensated by classical layers.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]