{"id":757,"date":"2023-03-13T13:06:45","date_gmt":"2023-03-13T12:06:45","guid":{"rendered":"https:\/\/www.autoqml.ai\/?page_id=757"},"modified":"2025-11-21T11:46:23","modified_gmt":"2025-11-21T10:46:23","slug":"publikationen","status":"publish","type":"page","link":"https:\/\/www.autoqml.ai\/en\/publikationen\/","title":{"rendered":"Publications"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"757\" class=\"elementor elementor-757\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5ec33f8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5ec33f8\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-03ef9d9\" data-id=\"03ef9d9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-170ad61 elementor-widget elementor-widget-text-editor\" data-id=\"170ad61\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Die im Rahmen des AutoQML-Forschungsprojekts gewonnenen Erkenntnisse wurden im Lauf des Projekts in einer Reihe von Publikationen als zentrale Forschungsergebnisse f\u00fcr die wissenschaftliche Fachwelt aufbereitet.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-535380a elementor-widget elementor-widget-text-editor\" data-id=\"535380a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul class=\"wp-block-list\"><li class=\"has-small-font-size\">D. Basilewitsch,\u00a0J. F. Bravo,\u00a0C. Tutschku, F. Struckmeier (2025). \u201eQuantum neural networks in practice: a comparative study with classical models from standard data sets to industrial images\u201c. In\u00a0Quantum Mach. Intell.\u00a07, 110.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s42484-025-00336-7\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1007\/s42484-025-00336-7\u00a0<\/a><\/li><li class=\"has-small-font-size\">D. Pranji\u0107, B. C. Mummaneni, C. Tutschku (2025). \u201cQuantum Annealing based Feature Selection\u201d. In: Neurocomputing,<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.neucom.2025.131673\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1016\/j.neucom.2025.131673<\/a>.<\/li><li class=\"has-small-font-size\">D. A. Kreplin, M. Willmann, J. Schnabel, F. Rapp, M. Hagel\u00fcken, M. Roth (2025). \u201csQUlearn: A Python Library for Quantum Machine Learning\u201d. In: IEEE Software 01, pp. 1\u20136.<br \/>url:\u00a0<a href=\"https:\/\/doi.ieeecomputersociety.org\/10.1109\/MS.2025.3527736\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.ieeecomputersociety.org\/10.1109\/MS.2025.3527736<\/a>.<\/li><li class=\"has-small-font-size\">M. Roth, D. A. Kreplin, D. Basilewitsch, J. F. Bravo, D. Klau, M. Marinov, D. Pranji\u0107, P. Schichtel, H. Stuehler, M. Willmann, M. Zoeller (2025). \u201eAutoQML: A Framework for Automated Machine Learning,\u201c in 2025 IEEE International Conference on Quantum Software (QSW), Helsinki, Finland, 2025, pp. 81-91,<br \/>url:\u00a0<a href=\"https:\/\/doi.ieeecomputersociety.org\/10.1109\/QSW67625.2025.00019\">https:\/\/doi.ieeecomputersociety.org\/10.1109\/QSW67625.2025.00019<\/a><\/li><li class=\"has-small-font-size\">H. St\u00fchler and D. Pranjic (2025) \u201eQuanten-maschinelle Lernmethoden in der Preisprognose von gebrauchten Baumaschinen\u201c. In\u00a0<em>Z<\/em>eitschrift f\u00fcr wirtschaftlichen Fabrikbetrieb, vol. 120, no. 5, pp. 352-357.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.1515\/zwf-2024-0163\">https:\/\/doi.org\/10.1515\/zwf-2024-0163<\/a><\/li><li class=\"has-small-font-size\">D. Pranji\u0107, B. C. Mummaneni, C. Tutschku (2024). \u201cQuantum Annealing based Feature Selection in Machine Learning\u201d.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2411.19609\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2411.19609<\/a>.<\/li><li class=\"has-small-font-size\">D. Basilewitsch, J. F. Bravo, C. Tutschku, F. Struckmeier (2024). \u201cQuantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images\u201d.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2411.19276\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2411.19276<\/a>.<\/li><li class=\"has-small-font-size\">F. Rapp and M. Roth (2024). \u201cQuantum Gaussian process regression for Bayesian optimization\u201d. In: Quantum Machine Intelligence 6.5 (1).<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s42484-023-00138-9\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1007\/s42484-023-00138-9<\/a>.<\/li><li class=\"has-small-font-size\">H. St\u00fchler, D. Klau, M.-A. Z\u00f6ller, A. Beiderwellen-Bedrikow, C. Tutschku (2024). \u201cEnd-to-End Implementation of Automated Price Forecasting Applications\u201d. In: SN Computer Science 5(402).<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s42979-024-02735-2\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1007\/s42979-024-02735-2<\/a>.<\/li><li class=\"has-small-font-size\">H. St\u00fchler, D. Pranji\u0107, Christian Tutschku (2024). \u201cEvaluating Quantum Support Vector Regression Methods for Price Forecasting Applications\u201d. In\u00a0Proceedings of the 16th International Conference on Agents and Artificial Intelligence \u2013 Volume 3: ICAART.<br \/>url:\u00a0<a href=\"https:\/\/www.scitepress.org\/Link.aspx?doi=10.5220\/0012351400003636\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.5220\/0012351400003636<\/a>.<\/li><li class=\"has-small-font-size\">J. Berberich, D. Fink, D. Pranji\u0107, C. Tutschku, C. Holm (2023). \u201cTraining robust and generalizable quantum models\u201d.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2311.11871\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2311.11871<\/a>.<\/li><li class=\"has-small-font-size\">D. Klau, H. Krause, D. A. Kreplin, M. Roth, C. Tutschku, M. Z\u00f6ller (2023). \u201cAutoQML \u2013 A Framework for Automated Quantum Machine Learning\u201d.<br \/>url:\u00a0<a href=\"https:\/\/www.digital.iao.fraunhofer.de\/content\/dam\/iao\/ikt\/de\/documents\/AutoQML_Framework.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.digital.iao.fraunhofer.de\/content\/dam\/iao\/ikt\/de\/documents\/AutoQML_Framework.pdf<\/a>.<\/li><li class=\"has-small-font-size\">D. Klau, M. Z\u00f6ller, C. Tutschku (2023). \u201cBringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms\u201d.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2310.04238\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2310.04238<\/a>.<\/li><li class=\"has-small-font-size\">H. St\u00fchler, M.-A. Z\u00f6ller, D. Klau, A. Beiderwellen-Bedrikow, C. Tutschku (2023). \u201cBenchmarking Automated Machine Learning Methods for Price Forecasting Applications\u201d.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.5220\/0012051400003541\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.5220\/0012051400003541<\/a>.<\/li><li class=\"has-small-font-size\">F. Rapp and M. Roth (2023). \u201eQuantum Gaussian Process Regression for Bayesian Optimization\u201c.<br \/>url:\u00a0<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2304.12923\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2304.12923<\/a>.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Die im Rahmen des AutoQML-Forschungsprojekts gewonnenen Erkenntnisse wurden im Lauf des Projekts in einer Reihe von Publikationen als zentrale Forschungsergebnisse f\u00fcr die wissenschaftliche Fachwelt aufbereitet.\u00a0 D. Basilewitsch,\u00a0J. F. Bravo,\u00a0C. Tutschku, F. Struckmeier (2025). \u201eQuantum neural networks in practice: a comparative study with classical models from standard data sets to industrial images\u201c. In\u00a0Quantum Mach. Intell.\u00a07, 110.url:\u00a0https:\/\/doi.org\/10.1007\/s42484-025-00336-7\u00a0 &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.autoqml.ai\/en\/publikationen\/\" class=\"more-link\">Read more<span class=\"screen-reader-text\"> &#8222;Publikationen&#8220;<\/span><\/a><\/p>","protected":false},"author":3,"featured_media":19,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-757","page","type-page","status-publish","has-post-thumbnail","hentry"],"featured_media_urls":{"thumbnail":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-150x150.jpeg",150,150,true],"medium":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-300x93.jpeg",300,93,true],"medium_large":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-768x239.jpeg",768,239,true],"large":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-1024x319.jpeg",950,296,true],"1536x1536":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-1536x478.jpeg",1536,478,true],"2048x2048":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-2048x637.jpeg",2048,637,true],"trp-custom-language-flag":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-18x6.jpeg",18,6,true],"inspiro-featured-image":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-2000x622.jpeg",2000,622,true],"inspiro-loop":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-950x320.jpeg",950,320,true],"inspiro-loop@2x":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-1900x640.jpeg",1900,640,true],"portfolio_item-thumbnail":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-600x400.jpeg",600,400,true],"portfolio_item-thumbnail@2x":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-1200x796.jpeg",1200,796,true],"portfolio_item-masonry":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-600x187.jpeg",600,187,true],"portfolio_item-masonry@2x":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-1200x373.jpeg",1200,373,true],"portfolio_item-thumbnail_cinema":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-800x335.jpeg",800,335,true],"portfolio_item-thumbnail_portrait":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-600x796.jpeg",600,796,true],"portfolio_item-thumbnail_portrait@2x":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-1200x796.jpeg",1200,796,true],"portfolio_item-thumbnail_square":["https:\/\/www.autoqml.ai\/wp-content\/uploads\/2022\/04\/6399-AutoQML_ke_Web-Bild-4096x1274-2-scaled-800x796.jpeg",800,796,true]},"_links":{"self":[{"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/pages\/757","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/comments?post=757"}],"version-history":[{"count":73,"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/pages\/757\/revisions"}],"predecessor-version":[{"id":1595,"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/pages\/757\/revisions\/1595"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/media\/19"}],"wp:attachment":[{"href":"https:\/\/www.autoqml.ai\/en\/wp-json\/wp\/v2\/media?parent=757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}