[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128059-en":3,"doc-seo-128059-105":31,"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":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},128059,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Beyond 0’s and 1’s - Exploring the Impact of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine Learning - Diplomarbeit","A diploma thesis at TU Wien investigates how noise, data encoding, and hyperparameter optimization affect the training and performance of variational quantum algorithms in quantum machine learning. Classical hardware slowdowns and rising compute demands motivate post-Moore alternatives, where QML aims to exploit quantum phenomena for improved speed and expressivity. Experiments compare configurations to identify effective strategies under realistic quantum hardware errors. Results show optimizer and data transformation choices strongly impact performance, while noise reduces overall quality and particularly affects the best-performing models.","Beyond 0’s and 1’s Exploring the Impact of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine  \nLearning  \nDIPLOMARBEIT  \nzur Erlangung des akademischen Grades  \nDiplom-Ingenieurin  \nim Rahmen des Studiums  \nData Science  \neingereicht von  \nSabrina Herbst, B.Sc.  \nMat rikelnummer 1 1807863  \nan der Fakultät für Informatik der Technischen Universität Wien  \nBetreuung: Univ. Prof. Dr. Ivona Brandi´c  \nMitwir kung: Vincenzo De Maio, Ph.D.  \nWien, 16. September 2023      \nSabrina Herbst Ivona Brandi´c  \nTechnische Universität Wien A-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nBeyond 0’s and 1’s Exploring the Impact of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine  \nLearning  \nDIPLOMA THESIS  \nsubmitted in partial fulfillment of the requirements for the degree of  \nDiplom-Ingenieurin  \nin  \nData Science  \nby  \nSabrina Herbst, B.Sc.  \nRegistration Number 1 1807863  \nto the Faculty of Informatics at the TU Wien  \nAdvisor: Univ. Prof. Dr. Ivona Brandi´c  \nAssistance: Vincenzo De Maio, Ph. D.  \nVienna, 16th September, 2023      \nSabrina Herbst Ivona Brandi´c  \nTechnische Universität Wien A-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nErklärung zur Verfassung der Arbeit  \nSabrina Herbst, B.Sc.  \nHiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe.  \nWien, 16. September 2023    \nSabrina Herbst  \nv  \nAcknowledgements  \nFirst and foremost, I would like to express my sincere gratitude to my supervisors, Prof. Ivona Brandic and Vincenzo De Maio, for their guidance and commitment throughout the entire research process and for the created opportunities.  \nI want to thank my sister Natalie for her feedback on early versions of the thesis. Additionally, I extend my heartfelt appreciation to everyone at the HPC lab at TU Wien for the wonderful discussions and occasional distractions from work.  \nThis work has been partially funded through the FFG Flagship project HPQC (High Performance Integrated Quantum Computing) \\# 897481 and the Standalone Project Transprecise Edge Computing (Triton), Austrian Science Fund (FWF): P 36870-N, 2023. IBM Quantum services were used for this work. The views expressed are personal ones, and do not reflect the official policy or position of IBM or the IBM Quantum team.  \nvii  \nKurzfassung  \nDa sich die Entwicklung klassischer (von-Neumann) Hardware verlangsamt und moderne Machine Learning (ML) Modelle zunehmend mehr Rechenleistung und Speicherplatz benötigen, versuchen Forscherinnen und Forscher neue Wege zu finden, aktuelle und zukünftige Anforderungen zu bewältigen. Neue Post-Moore Architekturen werden stetig weiterentwickelt, und, da sie eine vielversprechende Alternative, unter anderem aufgrund theoretischer algorithmischer Speedups, darstellen, hat die Bedeutung von Quantencomputern zugenommen. In diesem Rahmen versucht der Bereich des Quantum Machine Learning (QML), Quantenphänomene zu nutzen, um schnellere Algorithmen und eine bessere Expressivität zu erzielen.  \nVariational Quantum Algorithms (VQAs) haben aufgrund ihrer Eignung für aktuelle Quantenhardware Aufmerksamkeit erregt. VQAs besitzen jedoch zahlreiche Parameter, einschließlich Datentransformation, Architektur und Training, die die Modelle stark beeinflussen können. Zudem sind aktuelle Quantencomputer anfällig für Fehler, was das Training erschweren und die Leistung der Modelle verringern kann. In dieser Arbeit wollen wir verschiedene Konfigurationen vergleichen und ihre Performance in Gegenwart von ebendiesen Fehlern bewerten, um die effektivsten Ansätze zu ermitteln.  \nUnsere Experimente zeigen, dass die","cbCairGLqX0yaWTq","https://ap.wps.com/l/cbCairGLqX0yaWTq","pdf",3472633,3,1,155,"English","en",105,"# Acknowledgements\n# Kurzfassung\n## Motivation and background\n## Variational quantum algorithms and challenges\n## Experimental setup and evaluation\n## Key findings","[{\"question\":\"为什么研究噪声、数据编码和超参数优化对QML很重要？\",\"answer\":\"量子硬件容易出错且VQAs包含与数据变换、架构和训练相关的多种参数，这些因素会显著影响模型性能与训练难度，因此需要对其影响进行系统评估。\"},{\"question\":\"论文比较了哪些配置来评估性能？\",\"answer\":\"通过比较不同的优化器选择、数据变换方法及相关超参数配置，并在存在量子硬件错误的情况下评估模型表现，从而找出更有效的策略。\"},{\"question\":\"主要实验结论是什么？\",\"answer\":\"优化器与数据变换的选择会显著影响模型性能；仅靠数据变换不足以形成高效模型，需与良好配置组合。同时，量子噪声会降低整体性能，并尤其影响表现最好的模型。\"}]","Beyond 0’s and 1’s - Exploring the Impact of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine Learning - Diplomarbeit | PDF",1785944542,391,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"beyond-0s-and-1s-exploring-the-impact-of-noise-data-encoding-and-hyperparameter-optimization-in-quantum-machine-learning-diploma-thesis","",{"@graph":37,"@context":86},[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/beyond-0s-and-1s-exploring-the-impact-of-noise-data-encoding-and-hyperparameter-optimization-in-quantum-machine-learning-diploma-thesis/128059/",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","2026-08-28","2026-08-05",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},"为什么研究噪声、数据编码和超参数优化对QML很重要？","Question",{"text":76,"@type":77},"量子硬件容易出错且VQAs包含与数据变换、架构和训练相关的多种参数，这些因素会显著影响模型性能与训练难度，因此需要对其影响进行系统评估。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"论文比较了哪些配置来评估性能？",{"text":81,"@type":77},"通过比较不同的优化器选择、数据变换方法及相关超参数配置，并在存在量子硬件错误的情况下评估模型表现，从而找出更有效的策略。",{"name":83,"@type":74,"acceptedAnswer":84},"主要实验结论是什么？",{"text":85,"@type":77},"优化器与数据变换的选择会显著影响模型性能；仅靠数据变换不足以形成高效模型，需与良好配置组合。同时，量子噪声会降低整体性能，并尤其影响表现最好的模型。","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]