[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117356-en":3,"doc-seo-117356-105":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},117356,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Applications of Parameterized Quantum Circuits","With the development of near-term quantum devices, hybrid quantum-classical computing has been recognized as a promising framework for achieving near-term quantum advantages in chemistry, optimization, and machine learning tasks. Performance of such hybrid frameworks depends strongly on parameterized quantum circuits (PQCs). Designing PQC architectures that reliably exhibit quantum advantages for practical quantum machine learning remains challenging. This thesis studies the capability of PQCs in supporting effective quantum machine learning and advances methods for building more suitable circuit architectures.","Machine Learning Applications of  \nParameterized Quantum Circuits by Guangxi Li  \nThesis submitted in fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nunder the supervision of Profs. Sanjiang Li and Yuan Feng  \nUniversity of Technology Sydney  \nFaculty of Engineering and Information Technology  \nFeb 2023  \nCertificate of Original Authorship  \nI, Guangxi Li, declare that this thesis is submitted in fulfillment of the requirements for the award of Doctor of Philosophy, in the Faculty of Engineering and Information Technology at the University of Technology Sydney.  \nThis thesis is wholly my own work unless otherwise referenced or acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis.  \nThis document has not been submitted for qualifications at any other academic institution.  \nThis research is supported by the Australian Government Research Training Program.  \nProduction Note:  \nSignature: Signature removed prior to publication.  \nDate: 10/02/2023  \nTo my family.  \nAcknowledgements  \n[During my Ph.D. study](During my Ph.D. study), I enjoyed a lot of life in Sydney, learned a lot of new knowledge, and made myself grow in all aspects. However, [my Ph.D. study](my Ph.D. study) process was not smooth, especially under the attack of Covid-19, which led to a significant reduction in overall learning efficiency. Therefore, I cannot complete my studies successfully without the support and dedication of many people.  \nFirst and foremost, I would like to express my deepest gratitude to my supervisor, Prof. Sanjiang Li, for providing me with this opportunity to study at the Centre for Quantum Software and Information (CQSI), University of Technology Sydney. His rigorous academic style and patient character have helped me a lot and inspired me. Since I had not yet enrolled in the university, he has patiently guided me in matters related to my enrollment through multiple emails. After entering the university, he also helped me immensely in my research. He not only gave me total freedom in research contents and research directions but also often reminded me at the group meeting that I should not be impetuous in my research and should calm down to do fundamental research. It had a significant impact on my later research style. He also gave me a lot of help in my life, such as asking me if I was still used to eating and living and if I had gone to the surrounding places for sightseeing.  \nI am also grateful to my co-supervisor, Prof. Yuan Feng, for his solid academic skills and patient communication with me. He really knows a lot and often gave me a lot of discussions and helped me with the proof details I wrote, which also let me know that mathematical proof is very rigorous. Moreover, I am grateful to Prof. Mingsheng Ying, Prof. Zhengfeng Ji, Min-Hsiu Hsieh, Nengkun Yu, Christopher Ferrie and Yulei Sui for their helpful research guidance.  \nI would really like to thank Prof. Runyao Duan for supporting me with opportunities to visit Baidu Quantum Research Institute. At that time, due to Covid-19, I could not return to Sydney, which led to slow progress in my scientific research. Fortunately, Prof. Duan gave me the opportunity to visit Baidu, which not only enabled me to continue my research but also made me feel the strong research atmosphere in the company. I would especially like to thank my mentor, Dr. Xin Wang, for his academic guidance. His  \nacademic style and his style of not fearing difficulties attracted me profoundly and also inspired me to keep learning. His efficient behavior style also deeply affected me. He isnot only my mentor but also my friend and often talks with me about interesting things in the quantum field.  \nI would like to extend my sincere thanks to my co-authors or co-workers: Youle Wang, Xin Hong, Yu Luo, Xiangzhen Zhou, Zhixin Song, Xuanqiang Zhao, Ruilin Ye, Zhiwei Liu, Gang Tang, Afrad Basheer, Benchi Zhao, Zhan Yu, Hongshun Yao, Daokun Zhang,","cbCaikNwEYJm8SBS","https://ap.wps.com/l/cbCaikNwEYJm8SBS","pdf",4366302,1,159,"English","en",105,"# Abstract\n## Problem and context\n## Role of parameterized quantum circuits (PQCs)\n## Thesis contributions","[{\"question\":\"What computing framework does the thesis focus on?\",\"answer\":\"The thesis focuses on hybrid quantum-classical computing, aiming to leverage near-term quantum advantages for tasks such as chemistry, optimization, and machine learning.\"},{\"question\":\"Why are parameterized quantum circuits (PQCs) central to the research?\",\"answer\":\"The thesis states that the performance of hybrid quantum-classical frameworks significantly relies on the power of PQCs, because PQC design directly affects learning outcomes.\"},{\"question\":\"What challenge does the thesis address regarding PQCs?\",\"answer\":\"The thesis highlights the difficulty of designing PQC architectures that show quantum superiorities suitable for practical quantum machine learning tasks.\"}]","Machine Learning Applications of Parameterized Quantum Circuits | 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computing framework does the thesis focus on?","Question",{"text":75,"@type":76},"The thesis focuses on hybrid quantum-classical computing, aiming to leverage near-term quantum advantages for tasks such as chemistry, optimization, and machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are parameterized quantum circuits (PQCs) central to the research?",{"text":80,"@type":76},"The thesis states that the performance of hybrid quantum-classical frameworks significantly relies on the power of PQCs, because PQC design directly affects learning outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenge does the thesis address regarding PQCs?",{"text":84,"@type":76},"The thesis highlights the difficulty of designing PQC architectures that show quantum superiorities suitable for practical quantum machine learning 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