{"id":535,"date":"2025-02-27T10:34:27","date_gmt":"2025-02-27T09:34:27","guid":{"rendered":"https:\/\/flaqship.eu\/?page_id=535"},"modified":"2026-03-18T13:29:00","modified_gmt":"2026-03-18T12:29:00","slug":"publikationen","status":"publish","type":"page","link":"https:\/\/flaqship.eu\/en\/publikationen\/","title":{"rendered":"Publications"},"content":{"rendered":"<div style=\"height:211px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-aa403e6c\"><h2 class=\"uagb-heading-text\"><strong>Publications<\/strong><\/h2><\/div>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-8a5dafc6\"><h3 class=\"uagb-heading-text\"><strong>Studies<\/strong>: <\/h3><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>S. Loos, F. Bickert, M. Dotzel, C. K. Tutschku, &amp; S. Kaiser (2024). Potentials and Needs of the Quantum Computing Ecosystem. In O. Riedel, K. H\u00f6lzle, W. Bauer, &amp; B. Bienzeisler (Eds.), <br>url: <a href=\"https:\/\/doi.org\/10.24406\/publica-3632\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.24406\/publica-3632<\/a><\/li>\n\n\n\n<li>C. Tutschku, A. Sturm, F. Kn\u00e4ble, B. C. Mummaneni, D. Pranjic, C. Stephan, D. B. Mayer, G. Ko\u00dfmann, M. Roth, P.-A. Matt, A. Grigorjan, T. Wellens, K. K\u00f6nig, M. Beisel, F. Truger, F. Shagieva, O. Denninger, S. Garhofer (2023). \u201cQuantencomputing in der industriellen Applikation. Vom Algorithmen-, Markt- und Hardware\u00fcberblick hin zu anwendungszentriertem Quantensoftware-Engineering\u201c. <br>url: <a href=\"https:\/\/doi.org\/10.24406\/publica-805\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.24406\/publica-805<\/a><\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-875b97b3\"><h3 class=\"uagb-heading-text\"><strong>Scientific Publications <\/strong><\/h3><\/div>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-18dc6a82\"><h4 class=\"uagb-heading-text\"><strong>2026<\/strong><\/h4><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Y. Ji, M. Roth, D. A. Kreplin, I. Polian, F. K. Wilhelm (2025). &#8222;Data-Efficient Quantum Noise Modeling via Machine Learning&#8220;. In Phys. Rev. Applied\u00a025, 034051,<br>url: <a href=\"https:\/\/doi.org\/10.1103\/5r9m-y6z6\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1103\/5r9m-y6z6<\/a><\/li>\n\n\n\n<li>M. Hagelueken, D. A. Kreplin, F. Wieland, M. F. Huber, M. Roth (2026). &#8222;Exponential Scaling Barriers for Variational Quantum Eigensolvers&#8220;. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2603.13073\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2603.13073<\/a><a href=\"https:\/\/scirate.com\/arxiv\/2603.13073\/scites\"><\/a><\/li>\n\n\n\n<li>Y. Ji, Z.-Y. Chen, M. Roth, D. A. Krpelin, C. Schiffer, M. King, O. Anton, M. S. Alam, M. Krutzik, D. Willsch, L. Mathey, F. K. Wilhelm, G.-P. Guo (2026). &#8222;Quantum Deep Learning: A Comprehensive Review&#8220;. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2603.06644\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2603.06644<\/a><\/li>\n\n\n\n<li>K. Yamaguchi,&nbsp;L. Rullk\u00f6tter,&nbsp;I. Shehzad,&nbsp;S. J. Wagner,&nbsp;C. Tutschku,&nbsp;A. Kempf (2026). &#8222;Experimental demonstration that qubits can be cloned at will, if encrypted with a single-use decryption key&#8220;.<br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2602.10695\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2602.10695<\/a><\/li>\n\n\n\n<li>N. Schillo, A. Sturm, R. Quay (2026). \u201cBlock Encoding Linear Combinations of Pauli Strings Using the Stabilizer Formalism\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2601.05740\" target=\"_blank\" rel=\"noreferrer noopener\"> https:\/\/doi.org\/10.48550\/arXiv.2601.05740<\/a><\/li>\n\n\n\n<li>D. Pranji\u0107, B. C. Mummaneni, C. Tutschku (2026). &#8222;Quantum Annealing based Feature Selection\u201d. In: Neurocomputing 659.<br>url: <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>\n<\/ul>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-7e3c2a5d\"><h4 class=\"uagb-heading-text\"><strong>2025<\/strong><\/h4><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>F. Bickert, M.J. Maier, S. Kaiser, K. H\u00f6lzle (2025). \u201eKollaborative Mensch-KI-Interaktion f\u00fcr datengest\u00fctzte Vorausschau: Ein Human-in-the-Loop-Ansatz zum Trendmonitoring im Bereich Quantencomputing\u201c, 18. Symposium f\u00fcr Vorausschau und Technologieplanung, Berlin. <br>ISSN: <a href=\"https:\/\/www.google.com\/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=&amp;ved=2ahUKEwjnv_iLz7-SAxXt_rsIHWJXLT4QFnoECBgQAQ&amp;url=https%3A%2F%2Fpublica-rest.fraunhofer.de%2Fserver%2Fapi%2Fcore%2Fbitstreams%2F09b113c9-02c2-4b1f-8970-42e41a8d55a4%2Fcontent&amp;usg=AOvVaw0nAn1Y-G32R7e-9A_g_Vx0&amp;opi=89978449\" target=\"_blank\" rel=\"noreferrer noopener\">2195-5239<\/a><\/li>\n\n\n\n<li>D. Basilewitsch,\u00a0J. F. Bravo,\u00a0C. Tutschku, F. Struckmeier (2025). &#8222;Quantum 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<\/a><\/li>\n\n\n\n<li>L. Binkowski, G. Ko\u00dfmann, C. Tutschku, and R. Schwonnek (2025). &#8222;Symmetry-based quantum algorithms for open-shop scheduling with hard constraints&#8220;. Academia Quantum, 2(3). <br>url: <a href=\"https:\/\/doi.org\/10.20935\/AcadQuant7900\">https:\/\/doi.org\/10.20935\/AcadQuant7900<\/a> <\/li>\n\n\n\n<li>A. Sturm and N. Schillo (2025). &#8222;Efficient and Explicit Block Encoding of Finite Difference Discretizations of the Laplacian&#8220;.<br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2509.02429\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2509.02429<\/a><\/li>\n\n\n\n<li>L. Rullk\u00f6tter,&nbsp;S. Weber,&nbsp;V. M. Katukuri,&nbsp;C. Tutschku,&nbsp;B. C. Mummaneni (2025). &#8222;Resource-efficient Variational Block-Encoding&#8220;. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2507.17658\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2507.17658<\/a><\/li>\n\n\n\n<li>M. Albus, M. Roth, J. Schnabel, M. Willmann (2025). &#8222;Application of quantum annealing for scalable robotic assembly line optimization: a case study&#8220;. In: Procedia CIRP (134), pp. 25\u201330.<br>url: <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2212827125004986\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2212827125004986<\/a><\/li>\n\n\n\n<li>R. F. Ablan, M. Roth, J. Schnabel (2025). &#8222;On the similarity of bandwith-tuned quantum kernels and classcial kernels&#8220;. In: Quantum Sci. Technol.&nbsp;10&nbsp;035051. <br>url: <a href=\"https:\/\/doi.org\/10.1088\/2058-9565\/ade7ad\">https:\/\/doi.org\/10.1088\/2058-9565\/ade7ad<\/a><\/li>\n\n\n\n<li>M. Hagel\u00fcken, M. F. Huber, and M. Roth (2025). &#8222;Data Efficient Prediction of excited-state properties using Quantum Neural Networks&#8220;. In: New Journal of Physics 27(054508)<br>url: <a href=\"https:\/\/doi.org\/10.1088\/1367-2630\/add203\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1088\/1367-2630\/add203<\/a><\/li>\n\n\n\n<li>D. A. Kreplin, M. Willmann, J. Schnabel, F. Rapp, M. Hagel\u00fcken, M. Roth (2025). &#8222;sQUlearn: A Python Library for Quantum Machine Learning\u201d. In: IEEE Software 01, pp. 1\u20136. <br>url: <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>\n\n\n\n<li>S. S. Ram,&nbsp;M. Molli,&nbsp;V. M. Katukuri,&nbsp;B. C. Mummaneni (2025). &#8222;Optimizing Superconducting Qubit Performance: A Theoretical Framework for Design, Analysis, and Calibration\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2501.17825\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2501.17825<\/a><\/li>\n\n\n\n<li>F. Rapp, D. A. Kreplin, M. F. Huber, M. Roth (2025). &#8222;Reinforcement learning-based architecture search for quantum machine learning\u201d. In: Mach. Learn.: Sci. Technol. <br>url: <a href=\"https:\/\/doi.org\/10.1088\/2632-2153\/adaf75\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1088\/2632-2153\/adaf75<\/a><\/li>\n\n\n\n<li>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). &#8222;AutoQML: A Framework for Automated Machine Learning,&#8220; in 2025 IEEE International Conference on Quantum Software (QSW), Helsinki, Finland, 2025, pp. 81-91, <br>url: <a href=\"https:\/\/doi.ieeecomputersociety.org\/10.1109\/QSW67625.2025.00019\">https:\/\/doi.ieeecomputersociety.org\/10.1109\/QSW67625.2025.00019<\/a><\/li>\n\n\n\n<li>J. Schnabel and M. Roth (2025). &#8222;Quantum Kernel Methods Under Scrutiny: A Benchmarking Study&#8220;. In: Quantum Machine Intelligence 7(58).<br>url: <a href=\"https:\/\/doi.org\/10.1007\/s42484-025-00273-5\">https:\/\/doi.org\/10.1007\/s42484-025-00273-5<\/a><\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-dffde2d7\"><h4 class=\"uagb-heading-text\"><strong>2024<\/strong><\/h4><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>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: <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>\n\n\n\n<li>D. A. Kreplin and M. Roth (2024). \u201cReduction of finite sampling noise in quantum neural networks\u201d. In: Quantum 8, p. 1385. <br>url: <a href=\"https:\/\/doi.org\/10.22331\/q-2024-06-25-1385\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.22331\/q-2024-06-25-1385<\/a><\/li>\n\n\n\n<li>P.-A. Matt and M. Roth (2024). A heuristic for solving the irregular strip packing problem with quantum optimization. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2402.17542\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2402.17542<\/a><\/li>\n\n\n\n<li>B. C. Mummaneni, S. Chen, W. H\u00fcbner, G. Lefkidis (2024). \u201cInvestigation of the exact spin channels in laser-induced spin dynamics in two mononuclear Cu(ii) complexes\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.1039\/D4CP01086H\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1039\/D4CP01086H<\/a><\/li>\n\n\n\n<li>D. Pranji\u0107, F. Kn\u00e4ble, P. Kunst, D. Kutzias, D. Klau, C. Tutschku, L. Simon, M. Kraus, A. Abedi (2024). \u201cUnsupervised Quantum Anomaly Detection on Noisy Quantum Processors\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2411.16970\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2411.16970<\/a><\/li>\n\n\n\n<li>F. Rapp and M. Roth (2024). \u201cQuantum Gaussian process regression for Bayesian optimization\u201d. In: Quantum Machine Intelligence 6.5 (1). <br>url: <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>\n\n\n\n<li>N. Schillo (2024). \u201cQuantum Algorithms and Quantum Machine Learning for Differential Equations\u201d. <br>url: <a href=\"http:\/\/dx.doi.org\/10.18419\/opus-13866\" target=\"_blank\" rel=\"noreferrer noopener\">http:\/\/dx.doi.org\/10.18419\/opus-13866<\/a><\/li>\n\n\n\n<li>N. Schillo and A. Sturm (2024). \u201cQuantum Circuit Learning on NISQ Hardware\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2405.02069\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2405.02069<\/a><\/li>\n\n\n\n<li>A. Sturm, B. C. Mummaneni, L. Rullk\u00f6tter (2024). \u201cUnlocking Quantum Optimization: A Use Case Study on NISQ Systems\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2404.07171\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2404.07171<\/a><\/li>\n\n\n\n<li>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: <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>\n\n\n\n<li>H. St\u00fchler, D. Pranji\u0107, Christian Tutschku (2024). \u201cEvaluating Quantum Support Vector Regression Methods for Price Forecasting Applications\u201d. In&nbsp;Proceedings of the 16th International Conference on Agents and Artificial Intelligence &#8211; Volume 3: ICAART. <br>url: <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>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-369891bb\"><h4 class=\"uagb-heading-text\"><strong>2023<\/strong><\/h4><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>J. Berberich, D. Fink, D. Pranji\u0107, C. Tutschku, C. Holm (2023). \u201cTraining robust and generalizable quantum models\u201d. <br>url: <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>\n\n\n\n<li>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: <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>\n\n\n\n<li>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: <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>\n\n\n\n<li>G. Ko\u00dfmann, L. Binkowski, C. Tutschku, R. Schwonnek (2023). \u201cOpen-Shop Scheduling with Hard Constraints\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2211.05822\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2211.05822<\/a><\/li>\n\n\n\n<li>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: <a href=\"https:\/\/doi.org\/10.5220\/0012051400003541\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.5220\/0012051400003541<\/a><\/li>\n\n\n\n<li>A. Sturm (2023). \u201cTheory and Implementation of the Quantum Approximate Optimization Algorithm: A Comprehensible Introduction and Case Study Using Qiskit and IBM Quantum Computers\u201d. <br>url: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2301.09535\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2301.09535<\/a><\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-uagb-advanced-heading uagb-block-db436a39\"><h4 class=\"uagb-heading-text\"><strong>2022<\/strong><\/h4><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>P.-A. Matt, R. Ziegler, D. Barjovic, M. Roth, M. F. Huber (2022), \u201cA Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules\u201d. url: &nbsp;<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2209.07575\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.48550\/arXiv.2209.07575<\/a><\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Publikationen Studien: Wissenschaftliche Publikationen: 2026 2025 2024 2023 2022<\/p>","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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