[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290247-105":59,"doc-detail-290247-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","quantum-machine-learning-applied-to-astronomical-datasets","Quantum Machine Learning Applied to Astronomical Datasets","","This PhD dissertation investigates how quantum machine learning techniques can be applied in astronomy. It evaluates supervised and unsupervised tasks such as classification, clustering, and anomaly detection, using quantum kernel methods (including quantum-enhanced support vector machines and spectral clustering) and quantum variational circuits (including quantum convolutional neural networks and quantum autoencoders). Results across multiple astronomical datasets indicate quantum methods are competitive only in narrow cases. Classical machine learning generally outperforms quantum approaches, with noise on real devices further widening the gap. The dissertation recommends that QML not yet be relied on for astronomical applications in its current state.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/quantum-machine-learning-applied-to-astronomical-datasets/290247/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/quantum-machine-learning-applied-to-astronomical-datasets/290247.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What quantum machine learning approaches are evaluated in the dissertation?","Question",{"text":113,"@type":114},"The dissertation evaluates quantum kernel methods (e.g., quantum-enhanced support vector machines and quantum-enhanced spectral clustering) and quantum variational circuits (including quantum convolutional neural networks and quantum autoencoders).","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which astronomy-related tasks are included in the study?",{"text":118,"@type":114},"The study focuses on supervised and unsupervised tasks, including classification, clustering, and anomaly detection, across multiple astronomical datasets.",{"name":120,"@type":111,"acceptedAnswer":121},"What overall conclusion does the dissertation reach about using QML in astronomy?",{"text":122,"@type":114},"Quantum methods are only competitive in specific cases; classical methods generally outperform them, and real-device noise increases the performance gap. The recommendation is that QML should not yet be relied upon for astronomical applications in its current state.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290247,1789641126,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","Quantum Machine Learning Applied to Astronomical Datasets  \nby  \nDonovan Michael Slabbert  \nDissertation presented for the degree of PhD in Physics in the Faculty of Science at Stellenbosch University  \nSupervisor: Prof. Francesco Petruccione  \nMarch 2026  \nThe financial a ssistance o f t he S outh A frican Q uantum I nitiative ( SA Q uTI) t owards t his r esearch is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to SA QuTI.  \nDeclaration  \nBy submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.  \nDate:   March 2026    \nCopyright © 2026 Stellenbosch University All rights reserved.  \nArtificial Intelligence  \nI declare that certain sections of the text and tables herein, as well as the code created and used to complete this dissertation, were assisted by artificial intelligence tools, specifically Large Language Models ( LLMs) . The tools used were OpenAI’s ChatGPT and GitHub Copilot. These tools were used to accelerate the research and writing process. This was done through text refinement andrephrasing, skeleton code generation, bug fixing, b rainstorming, a nd literature searches. All AI-assisted content was critically and carefully reviewed and edited to ensure academic integrity, accuracy, and originality.  \nDate: March 2026  \nAbstract  \nQuantum Machine Learning Applied to Astronomical  \nDatasets  \nD. Slabbert  \nDepartment of Physics,  \nStellenbosch University,  \nStellenbosch, South Africa.  \nDissertation: PhD Physics  \nMarch 2026  \nThis dissertation investigates the application of quantum machine learning techniques in the field of a stronomy. The focus is o n a variety of supervised and unsupervised tasks, including classification, c lustering, and anomaly detection. Quantum kernel methods, such as quantum-enhanced support vector machines and quantum-enhanced spectral clustering, as well as quantum variational circuits, including quantum convolutional neural networks and quantum autoencoders, are evaluated in comparison to classical learning approaches across multiple astronomical datasets. The primary goal was to benchmark quantum machine learning (QML) methods and assess their feasibility for realworld applications.  \nThe studies show that quantum methods are only competitive in very specific cases; for example, when explicit feature representation or limited training data coincidentally favor the quantum approach. These observations are not general and do not hold in most scenarios. Typically, classical machine learning implementations consistently outperform quantum approaches in the majority of cases. Most implementations were simulated only; however, limited runs on current real quantum devices indicate that noise further amplifies the performance gap, reinforcing the disconnect between simulated QML results and practical implementations. Based on these findings, the recommendation is that QML should not yet be relied upon for astronomical applications in its current state. Real progress in applied quantum machine learning will likely require fault-tolerant quantum computers, faster data upload and readout times, and improved algorithms to make quantum approaches truly viable for practical astronomical tasks.  \nAcknowledgments  \nCrucial to the completion of this dissertation was the funding from the South African Quantum Technology Initiative (SA QuTI), through the Department of Science, Technology and Innovation of South Africa.  \nHowever demanding the path, I was fortunate to have the steadfast guidance of my supervisor, Prof. Francesco Petruccione. His encourag","cbCaitB3Tt3Bz8cz","https://ap.wps.com/l/cbCaitB3Tt3Bz8cz","pdf",9506599,239,"English","# Abstract\n## Acknowledgments\n## Dedication","[{\"question\":\"What quantum machine learning approaches are evaluated in the dissertation?\",\"answer\":\"The dissertation evaluates quantum kernel methods (e.g., quantum-enhanced support vector machines and quantum-enhanced spectral clustering) and quantum variational circuits (including quantum convolutional neural networks and quantum autoencoders).\"},{\"question\":\"Which astronomy-related tasks are included in the study?\",\"answer\":\"The study focuses on supervised and unsupervised tasks, including classification, clustering, and anomaly detection, across multiple astronomical datasets.\"},{\"question\":\"What overall conclusion does the dissertation reach about using QML in astronomy?\",\"answer\":\"Quantum methods are only competitive in specific cases; classical methods generally outperform them, and real-device noise increases the performance gap. The recommendation is that QML should not yet be relied upon for astronomical applications in its current state.\"}]","Quantum Machine Learning Applied to Astronomical Datasets | PDF",602]