[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125721-en":3,"doc-seo-125721-105":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},125721,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Bringing Quantum Algorithms to Automated Machine Learning - A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms","Quantum Computing (QC) is positioned as a fast-advancing approach for modern computation, especially in simulation and data-driven machine learning. At the same time, established Machine Learning (ML) workflows struggle with gaps between industrial needs and ML expertise, reproducibility issues, and inefficient prototyping. Automated Machine Learning (AutoML) frameworks target these problems by automating pipeline construction, data preprocessing, model training, and hyperparameter optimization. This work selects and benchmarks open-source AutoML frameworks by multi-phase criteria, evaluating their extensibility for Quantum Machine Learning (QML) and their performance across industrial ML use cases. An extended AutoQML framework is then proposed.","arXiv :2310 .04238v 1 [ cs .LG] 6 Oct 2023  \nDennis Klau 1 | Marc Zöller2 | Dr. Christian Tutschku 1  \n1 Fraunhofer IAO, Nobelstraße 12, 70569 Stuttgart, Germany  \n2 USU GmbH, Rüppurrer Str. 1, 76137 Karlsruhe, Germany  \nBringing Quantum Algorithms to Automated Machine Learning  \nA Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms  \nAbstract  \nQuantum Computing (QC) is becoming an increasingly promising technology for modern computation, especially in the field of data driven approaches like simulation and machine learning. With the high momentum of research and development of new hard-and software, QC holds a big promise in redefining many state-of-the-art computation approaches today.  \nOn the other hand, the nowadays well-established field of machine learning (ML) faces challenges like the discrepancy of demand by industry and availability of ML experts, reproducibility, and efficiency in prototyping. To overcome some of these issues, several frameworks have been created for automating the process of pipeline construction, data preprocessing, model training and hyperparameter optimization (HPO), many of them open source. In most cases, these Automated Machine Learning (AutoML) frameworks implement a fixed subset of known approaches and algorithms, or encapsulate an established ML backend, that defines the available algorithms.  \nThis work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. This is done by considering software selection approaches [1], as well as in terms of the technical perspective of AutoML [2, 3] .  \nThe requirements for the framework selection are divided into hard and soft criteria regarding their software and ML attributes. Additionally, a classification of AutoML frameworks is made into high-and low-level types, inspired by the findings of [4] . Finally, we select Ray and AutoGluon as the suitable low- and high-level frameworks respectively, as they fulfil all requirements sufficiently and received the best evaluation feedback during the use-case study.  \nBased on those findings, we build an extended Automated Quantum Machine Learning (AutoQML) framework with QC-specific pipeline steps and decision characteristics for hardware and software constraints.  \nContents  \n1 Introduction & Motivation 1  \n2 Systematic Review of AutoML 3  \n3 Automated Quantum Machine Learning (AutoQML) 6  \n4 Framework Selection Approach 8  \n5 Use-Case Study 11  \n5. 1 Use-Case Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11  \n5.1. 1 IAV GmbH Ingenieursgesellschaft Auto und Verkehr . . . . . . . . . 11  \n5.1.2 KEB Automation KG . . . . . . . . . . . . . . . . . . . . . . . . . 11  \n5.1.3 TRUMPF Werkzeugmaschinen GmbH + Co. KG . . . . . . . . . . 12  \n5.1.4 Zeppelin GmbH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n5.2 Study Evaluation ................................ 13  \n5.2.1 Quality Aspects ............................. 13  \n5.2.2 Resource and API aspects ....................... 14  \n5.2.3 Quantum Aspects ............................ 15  \n5.2.4 Additional Findings ........................... 16  \n6 Summary & Future Work 18  \n7 Appendix 19  \n7. 1 Candidate list of open-source frameworks . . . . . . . . . . . . . . . . . . . 19  \n7.2 Questionnaire of the ML expert interviews . . . . . . . . . . . . . . . . . . 19  \nReferences 20  \nContact 22  \nEditors  \nThomas Renner | Director Digital Business, Fraunhofer IAO  \nProf. Dr.-Ing. Oliver Riedel | Executive Director Fraunhofer IAO  \nIntroducti","cbCaitPiY16EcaqB","https://ap.wps.com/l/cbCaitPiY16EcaqB","pdf",1872385,1,25,"English","en",105,"# Introduction & Motivation\n# Systematic Review of AutoML\n# Automated Quantum Machine Learning (AutoQML)\n# Framework Selection Approach\n# Use-Case Study\n## Use-Case Description\n## Study Evaluation\n# Summary & Future Work\n# Appendix","[{\"question\":\"What problem does the document address in existing machine learning practice?\",\"answer\":\"It addresses challenges in ML such as mismatches between industry demand and expert availability, reproducibility concerns, and inefficiencies during prototyping.\"},{\"question\":\"How does the work evaluate AutoML frameworks?\",\"answer\":\"It applies a multi-phase, multi-criteria selection approach with hard and soft requirements, then benchmarks the resulting frameworks across industrial use cases with different ML problem types.\"},{\"question\":\"Which frameworks are selected and why?\",\"answer\":\"Ray and AutoGluon are selected as suitable low- and high-level frameworks, respectively, because they meet all requirements sufficiently and received the best evaluation feedback in the use-case study.\"}]","Bringing Quantum Algorithms to Automated Machine Learning - A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms | PDF",1785900842,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"bringing-quantum-algorithms-to-automated-machine-learning-a-systematic-review-of-automl-frameworks-regarding-extensibility-for-qml-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/bringing-quantum-algorithms-to-automated-machine-learning-a-systematic-review-of-automl-frameworks-regarding-extensibility-for-qml-algorithms/125721/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address in existing machine learning practice?","Question",{"text":75,"@type":76},"It addresses challenges in ML such as mismatches between industry demand and expert availability, reproducibility concerns, and inefficiencies during prototyping.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work evaluate AutoML frameworks?",{"text":80,"@type":76},"It applies a multi-phase, multi-criteria selection approach with hard and soft requirements, then benchmarks the resulting frameworks across industrial use cases with different ML problem types.",{"name":82,"@type":73,"acceptedAnswer":83},"Which frameworks are selected and why?",{"text":84,"@type":76},"Ray and AutoGluon are selected as suitable low- and high-level frameworks, respectively, because they meet all requirements sufficiently and received the best evaluation feedback in the use-case study.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]