[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117495-en":3,"doc-seo-117495-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},117495,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Data Encoding, Compilation, and Algorithms for Quantum Machine Learning - Dissertation","Quantum computing offers substantial promise for data processing, especially in quantum machine learning, where data must be represented through carefully chosen quantum encodings synthesized by quantum operations. This dissertation develops both theory and practical methods for quantum data encodings, extending QROM beyond binary basis representations and improving data parallelism and efficiency. It also presents compilation strategies for quantum random number generators to support post-quantum cryptography, and explores equivariant encodings via Cayley graph representations. Robustness techniques target noise resilience and trainability, with experimental evidence improving training efficiency and model stability.","Southern Methodist University  \nSMU Scholar  \n\n| Computer Science and Engineering Theses and Dissertations | Computer Science and Engineering |\n| --- | --- |\n| Spring 5-17-2025\u003Cbr>Data Encoding, Compilation, and Algorithms for Quantum Machine Learning\u003Cbr>Aviraj Sinha\u003Cbr>Southern Methodist University, [avirajs@smu.edu](avirajs@smu.edu)\u003Cbr>Follow this and additional works at: [https://scholar.smu.edu/engineering_compsci_etds](https://scholar.smu.edu/engineering_compsci_etds)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, Data Science Commons, Quantum Physics Commons, and the Theory and Algorithms Commons |  |\n\nRecommended Citation  \nSinha, Aviraj, \"Data Encoding, Compilation, and Algorithms for Quantum Machine Learning\" (2025) . Computer Science and Engineering Theses and Dissertations. 48.  \n[https://scholar.smu.edu/engineering_compsci_etds/48](https://scholar.smu.edu/engineering_compsci_etds/48)  \nThis Dissertation is brought to you for free and open access by the Computer Science and Engineering at SMU Scholar. It has been accepted for inclusion in Computer Science and Engineering Theses and Dissertations by an authorized administrator of SMU Scholar. For more information, please visit [http://digitalrepository.smu.edu](http://digitalrepository.smu.edu).  \nDATA ENCODING, COMPILATION, AND ALGORITHMS FOR QUANTUM  \nMACHINE LEARNING  \nApproved by:  \n\n| Dr. Mitch Thornton, Ph.D.\u003Cbr>ECE\u003Cbr>Dissertation Committee Chairperson |\n| --- |\n| Dr. Eric C. Larson, Ph.D.\u003Cbr>CS |\n| Dr. Sanjaya Lohani, Ph.D.\u003Cbr>ECE |\n| Dr. Sukumaran Nair, Ph.D.\u003Cbr>ECE |\n\nDr. Jennifer Dworak, Ph.D. ECE  \nDATA ENCODING, COMPILATION, AND ALGORITHMS FOR QUANTUM  \nMACHINE LEARNING  \nA Dissertation Presented to the Graduate Faculty of the Lyle School of Engineering  \nSouthern Methodist University  \nin  \nPartial Fulfillment of the Requirements  \nfor the degree of  \nDoctor of Philosophy  \nwith a  \nMajor in Computer Engineering  \nby  \nAviraj Sinha  \nB.S, CS, Southern Methodist University  \nMS, Southern Methodist University  \nMay 17, 2025  \nCopyright (2025) Aviraj Sinha All Rights Reserved  \nSinha, Aviraj B.S, CS, Southern Methodist University, 2019  \nMS, Southern Methodist University, 2021  \nData Encoding, Compilation, and Algorithms for Quantum Machine Learning Advisor: Dr. Mitch Thornton, Ph.D.  \nDoctor of Philosophy conferred May 17, 2025 Dissertation completed April 10, 2025  \nQuantum computing holds significant potential for data processing, particularly in quantum machine learning. As in classical computing, diverse data encoding methods are essential for representing information. However, unlike classical systems, quantum data must be synthesized through quantum operations and can exist in a superposition of states. The choice of encoding directly impacts data efficiency, noise resilience, and trainability—key factors for effective quantum machine learning.  \nThis dissertation advances both the theory and application of quantum data encodings. It extends quantum read-only memory (QROM) beyond traditional binary basis representations, enhancing data parallelism and improving efficiency in quantum information processing. Additionally, it introduces novel compilation methods for quantum random number generators (QRNGs), broadening their application to the generation of non-parametric distributions for post-quantum cryptography. The research further explores equivariant data encodings using Cayley graph representations to extract spectral features on quantum computers, enabling more sophisticated quantum information processing and machine learning applications.  \nTo improve noise resilience and trainability, this work presents techniques for enhancing the robustness of quantum data encodings, facilitating better error mitigation in practical quantum simulations. The proposed compilation and parallelization strategies are applied to quantum kernel computations and quantum feature matching, demonstrating their effectiveness in machine learning tasks. Ex","cbCairZUGljlQqCK","https://ap.wps.com/l/cbCairZUGljlQqCK","pdf",22719784,1,288,"English","en",105,"# Table of Contents\n## List of Figures\n## List of Tables\n## Acknowledgments\n## Chapter 1\n## Acronyms\n## Chapter 2\n## Introduction\n## Chapter 3\n## Background\n## Data Terminology\n## Quantum Principles\n## Quantum Data Quality","[{\"question\":\"How does the dissertation characterize the role of data encoding in quantum machine learning?\",\"answer\":\"It explains that quantum data must be synthesized through quantum operations and that the encoding choice affects data efficiency, noise resilience, and trainability. These factors determine how effectively quantum models can learn and generalize.\"},{\"question\":\"What new capabilities are introduced by extending quantum read-only memory (QROM)?\",\"answer\":\"The work extends QROM beyond traditional binary basis representations, enabling greater data parallelism. This improves efficiency in quantum information processing.\"},{\"question\":\"How does the research improve noise resilience and training efficiency in practical quantum settings?\",\"answer\":\"It presents techniques that enhance the robustness of quantum data encodings to support better error mitigation. The proposed compilation and parallelization strategies are applied to quantum kernel computations and quantum feature matching, and experiments show improved training efficiency and noise robustness.\"}]","Data Encoding, Compilation, and Algorithms for Quantum Machine Learning - Dissertation | PDF",1785676316,726,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"data-encoding-compilation-and-algorithms-for-quantum-machine-learning-dissertation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/data-encoding-compilation-and-algorithms-for-quantum-machine-learning-dissertation/117495/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the dissertation characterize the role of data encoding in quantum machine learning?","Question",{"text":76,"@type":77},"It explains that quantum data must be synthesized through quantum operations and that the encoding choice affects data efficiency, noise resilience, and trainability. These factors determine how effectively quantum models can learn and generalize.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What new capabilities are introduced by extending quantum read-only memory (QROM)?",{"text":81,"@type":77},"The work extends QROM beyond traditional binary basis representations, enabling greater data parallelism. This improves efficiency in quantum information processing.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the research improve noise resilience and training efficiency in practical quantum settings?",{"text":85,"@type":77},"It presents techniques that enhance the robustness of quantum data encodings to support better error mitigation. The proposed compilation and parallelization strategies are applied to quantum kernel computations and quantum feature matching, and experiments show improved training efficiency and noise robustness.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]