[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118100-en":3,"doc-seo-118100-105":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},118100,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Training Theory of Variational Quantum Machine Learning","Training theory for variational quantum machine learning is presented through a PhD dissertation conducted at the University of Sydney. The work focuses on deep variational quantum circuits, addressing key optimization challenges and demonstrating trainability behavior using newly developed theoretical perspectives. Published results included training approaches that avoid barren-plateau effects via barren-plateau-free architectures and Gaussian initializations, plus an effective theory for training quantum neural networks at modest depths. Authorship and supervisory attribution are documented alongside related publications.","Training Theory of Variational Quantum Machine Learning  \nKAINING ZHANG  \nSID:   \nSupervisor: Prof. Dacheng Tao  \nAssociate Supervisor: Dr. Liu Liu  \nA thesis submitted in partial fulfilment of requirements for the degree of  \nDoctor of Philosophy  \nSchool of Computer Science Faculty of Engineering  \nUniversity of Sydney  \n2023  \nTo my family.  \nStatement of Authorship/Originality  \nI certify that the work contained in this thesis has not been previously submitted for a degree nor has it been submitted as a part of the requirements for other degree except as fully acknowledged within the text.  \nI also certify that this thesis has been written by me and the intellectual content of this thesis is the product of my own work. All the assistance received in preparing this thesis and sources have been acknowledged.  \nKaining Zhang  \nSchool of Computer Science  \nFaculty of Engineering  \nThe University of Sydney  \nMay 16, 2024  \nAuthorship Attribution Statement  \nThis thesis was conducted at the University of Sydney, under the supervision of Prof. Dacheng Tao and Dr. Liu Liu, between 2020 and 2023 . The main results presented in this dissertation were first introduced in the following publications:  \n(1) Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, Dacheng Tao,“Deep variational quantum circuits with barren-plateau-free architectures”, Under Review, 2023 . This paper is presented in Chapter 1.1.1 and Chapter 2. I designed the research, implemented the experiment, co-conducted the research, and wrote the draft manuscript.  \n(2) Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, Dacheng Tao,“Escaping from the barren plateau via Gaussian initializations in deep variational quantum circuits”, Annual Conference on Neural Information Processing Systems (NeurIPS), 2022 . This paper is presented in Chapter 1.1.1, Chapter 1.3, Chapter 1.4, and Chapter 3. I designed and conducted the research, implemented the experiment, co-conducted the research, and wrote the draft manuscript.  \n(3) Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, Dacheng Tao,“Effective theory of training quantum neural networks with modest depths”, Manuscript, 2023 . This paper is presented in Chapter 1.1.2 and Chapter 4. I designed and conducted the research, implemented the experiment, co-conducted the research, and wrote the draft manuscript.  \nOther publications I made contributions during my PhD course are listed as follows:  \n(1) Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, Dacheng Tao,“Quantum Gram-Schmidt processes and their application to efficient state readout for quantum algorithms”, Physical Review Research, 2021, 3(4): 043095 . 2021.  \n(2) Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, Dacheng Tao,“Toward trainability of quantum neural networks”, arXiv preprint arXiv:2011.06258 . 2020.  \nAUTHORSHIP ATTRIBUTION STATEMENT vii  \n(3) Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao,“Quantum Imitation Learning”, IEEE Transactions on Neural Networks and Learning Systems. 2023.  \n(4) Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao,“Offline quantum reinforcement learning in a conservative manner”, Proceedings of the AAAI Conference on Artificial Intelligence. 2023.  \n(5) Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao,“Recent advances for quantum neural networks in generative learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023.  \nIn addition to the statements above, in cases where I am not the corresponding author of a published item, permission to include the published material has been granted by the corresponding author.  \nKaining Zhang  \nSchool of Computer Science  \nFaculty of Engineering  \nThe University of Sydney  \nMay 16, 2024  \nAs supervisor for the candidature upon which this thesis is based, I can confirm that the authorship attribution statements above are correct.  \nDacheng Tao  \nSchool of Computer Science  \nFaculty of Engineering  \nThe ","cbCaih2GrLwbDUUt","https://ap.wps.com/l/cbCaih2GrLwbDUUt","pdf",8896700,1,168,"English","en",105,"# Authorship and Originality\n# Authorship Attribution Statement\n## Prior Publications and Chapter Mapping\n# Acknowledgements","[{\"question\":\"Where was the thesis conducted and who supervised it?\",\"answer\":\"The thesis was conducted at the University of Sydney under the supervision of Prof. Dacheng Tao and Dr. Liu Liu.\"},{\"question\":\"Which publication first introduced the main results presented in the dissertation?\",\"answer\":\"The main results were first introduced in “Deep variational quantum circuits with barren-plateau-free architectures” (under review, 2023), which is mapped to Chapter 1.1.1 and Chapter 2.\"},{\"question\":\"What training issue does the thesis aim to address in variational quantum circuits?\",\"answer\":\"The dissertation addresses trainability challenges related to barren plateaus, including architectures and initialization strategies that help avoid or escape barren-plateau behavior.\"}]","Training Theory of Variational Quantum Machine Learning | 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was the thesis conducted and who supervised it?","Question",{"text":75,"@type":76},"The thesis was conducted at the University of Sydney under the supervision of Prof. Dacheng Tao and Dr. Liu Liu.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which publication first introduced the main results presented in the dissertation?",{"text":80,"@type":76},"The main results were first introduced in “Deep variational quantum circuits with barren-plateau-free architectures” (under review, 2023), which is mapped to Chapter 1.1.1 and Chapter 2.",{"name":82,"@type":73,"acceptedAnswer":83},"What training issue does the thesis aim to address in variational quantum circuits?",{"text":84,"@type":76},"The dissertation addresses trainability challenges related to barren plateaus, including architectures and initialization strategies that help avoid or escape barren-plateau 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