[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127707-en":3,"doc-seo-127707-105":31,"detail-sidebar-cat-0-en-105":96},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127707,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Investigation of Quantum Computers for Quantum Simulation and Machine Learning - Doctoral Thesis - 2024","Quantum mechanical phenomena offer information-processing capabilities that may tackle computational problems intractable for classical computers. This doctoral thesis investigates three quantum-computing applications for physical and computational sciences. It presents two algorithms for simulating Lindblad-governed open quantum systems using quantum imaginary-time evolution to realize unitary implementations on contemporary hardware. It then develops efficient protocols to probe measurement-induced phase transitions in monitored superconducting circuits, reaching up to 22 physical qubits. Finally, it proves rigorous quantum advantages for adversarially robust classification, establishing conditions where classical learners cannot outperform chance while quantum learners achieve high-accuracy robust predictions.","Investigation of quantum computers for quantum simulation and machine learning  \nThesis by  \nHirsh Kamakari  \nIn Partial Fulfillment of the Requirements for the Degree of  \nDoctor of Philosophy  \nCALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California  \n2024  \nDefended March 13, 2024  \nii  \n© 2024  \nHirsh Kamakari  \nORCID: 0000-0002-5377-9631  \nAll rights reserved except where otherwise noted  \niii  \nACKNOWLEDGEMENTS  \nI am deeply thankful to my advisor, Prof. Austin J. Minnich, for his guidance and support throughout my Ph.D. His steadfast commitment to the development of his students has helped me grow both in terms of research and in the communication of my work. His dedication to creating a positive research environment both within the group and with collaborators has allowed me to freely explore my research interests.  \nI am thankful to Prof. Andrei Faraon, Prof. Xie Chen, and Prof. Manuel Endres for serving on my defense committee.  \nI am also grateful to Sonya, Kristen, Lynn, Jennifer, and Christy, our department, options, and group administrators for taking care of all essential non-research duties.  \nI am very thankful to my collaborators, Mario, Tanvi, Ryan, Abhinav, and Yaodong, for their mentorship and invaluable discussions and contributions to the projects we worked on together.  \nI am infinitely grateful for the friends I have made during my Ph.D. journey. They will remain unnamed just in case I forget anyone. Without their friendship I would not have discovered so many places in and around Pasadena, California, the US, and Mexico. They have all brought me lots of laughter and good times, and meeting all of them has been one of the main highlights of my five years at Caltech.  \nFinally, I would like to thank my family for supporting me from the moment I was born all the way to the writing of this sentence and beyond. This journey would not have been possible without them.  \niv  \nABSTRACT  \nThe use of quantum mechanical phenomena for information processing has the potential to solve computational problems which are believed to be intractable for classical computers. Inspired by this potential, the last several decades has seen rapid development in both the theory and practice of quantum information processing. In this thesis, we explore three applications of quantum computing for the physical and computational sciences.  \nThe first potential application is for the simulation of open quantum systems. We introduce two algorithms for the simulation of open quantum systems governed by a Lindblad equation. Based on adaptations of the quantum imaginary time evolution algorithm, these methods transform non-unitary open system evolution into unitary evolution which can be implemented on contemporary quantum hardware. We demonstrate these algorithms on IBM’s quantum hardware via the simulation of the spontaneous emission of a two-level system and the dissipative transverse field Ising model.  \nNext, we explore efficient methods to probe measurement induced phase transitions using superconducting circuits. These phase transitions occur in monitored quantum systems as the measurement rate of randomized single qubit measurements increases. We overcome two exponential bottlenecks which limited the system sizes of previous experiments on superconducting circuits by employing a cross-entropy benchmarking protocol and Clifford based circuit compression techniques. We observed measurement induced phase transitions on systems of up to 22 physical qubits.  \nFinally, we switch our attention to machine learning, where we prove rigorous quantum advantages for adversarially robust classification. By constructing a learning task based on widely accepted cryptographic assumptions, we show a necessary condition for the utility of quantum computers for robust classification. In particular, we show that for the learning task we construct, any efficient classical learner cannot robustly classify better than chance, whereas a quantum learner ","cbCaivu3mEo0n407","https://ap.wps.com/l/cbCaivu3mEo0n407","pdf",2457795,3,1,128,"English","en",105,"# Acknowledgements\n# Abstract\n# Published Content and Contributions\n# Table of Contents\n# List of Illustrations\n# List of Tables","[{\"question\":\"What are the three applications of quantum computing explored in the thesis?\",\"answer\":\"The thesis investigates quantum algorithms for simulating open quantum systems, protocols for measuring measurement-induced phase transitions in monitored superconducting circuits, and rigorous results on quantum advantages for adversarially robust classification.\"},{\"question\":\"How does the thesis simulate Lindblad-governed open quantum systems on quantum hardware?\",\"answer\":\"It introduces two algorithms that adapt quantum imaginary-time evolution to convert non-unitary open-system dynamics into unitary evolution, enabling implementation on contemporary quantum processors.\"},{\"question\":\"How are measurement-induced phase transitions probed in superconducting circuits?\",\"answer\":\"The thesis uses efficient benchmarking and circuit-compression techniques to overcome exponential bottlenecks, allowing observation of phase transitions as the measurement rate of randomized single-qubit measurements increases.\"},{\"question\":\"What does the thesis claim about quantum advantages for adversarially robust classification?\",\"answer\":\"It constructs a learning task based on widely accepted cryptographic assumptions and proves a necessary condition: any efficient classical learner cannot robustly classify better than chance, while a quantum learner can efficiently and robustly achieve high accuracy.\"}]","Investigation of Quantum Computers for Quantum Simulation and Machine Learning - Doctoral Thesis - 2024 | PDF",1785941085,323,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":29},"investigation-of-quantum-computers-for-quantum-simulation-and-machine-learning-doctoral-thesis-2024","",{"@graph":37,"@context":90},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/investigation-of-quantum-computers-for-quantum-simulation-and-machine-learning-doctoral-thesis-2024/127707/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What are the three applications of quantum computing explored in the thesis?","Question",{"text":76,"@type":77},"The thesis investigates quantum algorithms for simulating open quantum systems, protocols for measuring measurement-induced phase transitions in monitored superconducting circuits, and rigorous results on quantum advantages for adversarially robust classification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis simulate Lindblad-governed open quantum systems on quantum hardware?",{"text":81,"@type":77},"It introduces two algorithms that adapt quantum imaginary-time evolution to convert non-unitary open-system dynamics into unitary evolution, enabling implementation on contemporary quantum processors.",{"name":83,"@type":74,"acceptedAnswer":84},"How are measurement-induced phase transitions probed in superconducting circuits?",{"text":85,"@type":77},"The thesis uses efficient benchmarking and circuit-compression techniques to overcome exponential bottlenecks, allowing observation of phase transitions as the measurement rate of randomized single-qubit measurements increases.",{"name":87,"@type":74,"acceptedAnswer":88},"What does the thesis claim about quantum advantages for adversarially robust classification?",{"text":89,"@type":77},"It constructs a learning task based on widely accepted cryptographic assumptions and proves a necessary condition: any efficient classical learner cannot robustly classify better than chance, while a quantum learner can efficiently and robustly achieve high accuracy.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":47,"category_name":142,"show_sort_weight":111,"slug":143},19,"General","general"]