[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117797-en":3,"doc-seo-117797-105":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},117797,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning Applications for the Study and Control of Quantum Systems","This thesis investigates supervised, unsupervised, and reinforcement learning as tools for studying and controlling quantum systems. Classical machine learning methods are combined with ideas and capabilities from quantum devices and condensed-matter physics to adapt models to physical systems. Supervised learning is used for detecting quantum vortices in rotating Bose–Einstein condensates under noisy conditions. Unsupervised learning introduces a quantum anomaly detection framework with parameterized quantum circuits to map phase diagrams. Reinforcement learning applications include Q-learning for entanglement generation in quantum walks and an MPS-based trainable ansatz enabling larger quantum many-body system sizes than conventional neural-network approaches.","Machine Learning Applications for the Study and Control of Quantum Systems  \n\n| Author | Friederike Metz |\n| --- | --- |\n| Degree Conferral Date | 2023-02-28 |\n| Degree | Doctor of Philosophy |\n| Degree Referral Number | 38005甲第118号 |\n| Copyright Information | (C) 2023 The Author. |\n| URL | [http://doi.org/10.15102/1394.00002632](http://doi.org/10.15102/1394.00002632) |\n\nOkinawa Institute of Science and Technology Graduate University  \nThesis submitted for the degree  \nDoctor of Philosophy  \nMachine Learning Applications for the Study and Control of Quantum Systems  \nby  \nFriederike Metz  \nSupervisor: Professor Thomas Busch  \nJan 2023  \nDeclaration of Original and Sole Authorship  \nI, Friederike Metz, declare that this thesis entitled Machine Learning Applications for the Study and Control of Quantum Systems and the data presented in it are original and my own work.  \nI confirm that:  \n• No part of this work has previously been submitted by me for a degree at this or any other university.  \n• References to the work of others have been clearly acknowledged. Quotations from the work of others have been clearly indicated, and attributed to them.  \n• In cases where others have contributed to part of this work, such contribution has been clearly acknowledged and distinguished from my own work.  \n• None of this work has been previously published elsewhere, with the exception of the following:  \n– Chapter 2 has been published as [1]:  \nFriederike Metz, Juan Polo, Natalya Weber, and Thomas Busch Deep-learning-based quantum vortex detection  \nin atomic Bose–Einstein condensates  \nMachine Learning: Science and Technology 2 , 035019 (2021)  \nI implemented, trained, and evaluated the machine learning and wrote a first draft of the manuscript. All authors contributed to the discussions and to the editing of the manuscript draft.  \n– Chapter 3 has been published as [2]:  \nKorbinian Kottmann∗ , Friederike Metz∗ , Joana Fraxanet, Niccolò  \nBaldelli  \nVariational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer Phys. Rev. Research 3 , 043184 (2021)  \n∗ indicates co-first authorship  \nI implemented the quantum autoencoder in Qiskit (Python) and worked on the VQAD simulations for the TLFI and DEBHM model. All authors contributed to the discussions, the interpretation of the results, and the writing of the manuscript.  \n– Chapter 4 has been published as [3]:  \nAikaterini Gratsea, Friederike Metz, and Thomas Busch Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks Journal of Physics A: Mathematical and Theoretical 53 , 445306 (2020) I supervised this work and provided guidance, knowledge on reinforcement learning, and feedback. Furthermore, I contributed to the interpretation of the results, the writing of the manuscript, produced the final plots, and derived the asymptotic limit of the universally entangling coin sequence. All authors contributed to the discussions and the editing of the manuscript.  \n– Chapter 5 has been put as a preprint article on the arXiv [4]:  \nFriederike Metz and Marin Bukov  \nSelf-Correcting Quantum Many-Body Control using  \nReinforcement Learning with Tensor Networks  \narXiv:2201.11790 [quant-ph] (2022)  \nI performed the numerical simulations, the theoretical analysis, and wrote a first version of the manuscript. All authors contributed to the discussions, the interpretation of the results, and the editing of the manuscript draft.  \n• All of the above articles have been published in an open access format under the‘Creative Commons Attribution 4.0 International’ license and I have permission to reprint them for the purpose of the thesis.  \n• During my PhD I also contributed to two different works, which are not part of this thesis [5, 6] . Their references are:  \nRashi Sachdeva∗ , Friederike Metz∗ , Manpreet Singh,  \nTapan Mishra, and Thomas Busch Two-leg-ladder Bose-Hubbard models with staggered fluxes Phys. Rev. A 98 , 063612 (","cbCainU68AVQ1tMH","https://ap.wps.com/l/cbCainU68AVQ1tMH","pdf",40291237,1,174,"English","en",105,"# Abstract\n# Acknowledgment\n# Declaration of Original and Sole Authorship","[{\"question\":\"Which machine learning paradigms are covered in the thesis?\",\"answer\":\"The thesis addresses three main paradigms: supervised learning, unsupervised learning, and reinforcement learning, and shows how each can be applied to quantum systems.\"},{\"question\":\"How is supervised learning used in the first project?\",\"answer\":\"Supervised learning techniques from classical object detection are used to locate quantum vortices in rotating Bose–Einstein condensates, achieving high accuracy even under noise.\"},{\"question\":\"What does the thesis propose for unsupervised quantum anomaly detection?\",\"answer\":\"It introduces a quantum anomaly detection framework based on parameterized quantum circuits to map out phase diagrams of quantum many-body systems directly on a quantum computer without prior phase knowledge.\"}]","Machine Learning Applications for the Study and Control of Quantum Systems | 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machine learning paradigms are covered in the thesis?","Question",{"text":75,"@type":76},"The thesis addresses three main paradigms: supervised learning, unsupervised learning, and reinforcement learning, and shows how each can be applied to quantum systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is supervised learning used in the first project?",{"text":80,"@type":76},"Supervised learning techniques from classical object detection are used to locate quantum vortices in rotating Bose–Einstein condensates, achieving high accuracy even under noise.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the thesis propose for unsupervised quantum anomaly detection?",{"text":84,"@type":76},"It introduces a quantum anomaly detection framework based on parameterized quantum circuits to map out phase diagrams of quantum many-body systems directly on a quantum computer without prior phase 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