[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290918-105":59,"doc-detail-290918-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","modeling-feature-maps-for-quantum-machine-learning","Modeling Feature Maps for Quantum Machine Learning","","Quantum Machine Learning (QML) targets complex problems such as genome sequence classification, yet practical deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware is constrained by quantum noise. This study systematically analyzes how noise types—including dephasing, amplitude damping, depolarizing, thermal noise, bit flip, and phase flip—impact core QML algorithms (QSVC, PegQSVC, QNN, VQC) and feature maps (ZFeatureMap, ZZFeatureMap, PauliFeatureMap). Results show QSVC’s comparative robustness, while PegQSVC and QNN are more noise-sensitive, with PauliFeatureMap proving especially vulnerable.",{"@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/modeling-feature-maps-for-quantum-machine-learning/290918/",{"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/modeling-feature-maps-for-quantum-machine-learning/290918.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which quantum noise models are evaluated in the study?","Question",{"text":113,"@type":114},"The study evaluates dephasing, amplitude damping, depolarizing, thermal noise, bit flip, and phase flip noise models and tests their effects on QML performance.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do different QML algorithms respond to noise?",{"text":118,"@type":114},"QSVC is reported as notably robust under noise, while PegQSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise.",{"name":120,"@type":111,"acceptedAnswer":121},"Why is feature map selection important under noisy conditions?",{"text":122,"@type":114},"The results indicate that feature maps can strongly affect classification accuracy under noise: PauliFeatureMap is especially vulnerable, demonstrating the difficulty of maintaining reliable learning without noise mitigation.","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},290918,1789642950,{"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},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Modeling Feature Maps for Quantum Machine  \nLearning  \nNavneet Singh and Shiva Raj Pokhrel, IEEE, Senior Member  \narXiv :2501 .08205v1 [ cs .LG] 14 Jan 2025  \nAbstract—Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This study systematically evaluates how various quantum noise models—including dephasing, amplitude damping, depolarizing, thermal noise, bitflip, and phase-flip—affect key QML algorithms (QSVC, PegQSVC, QNN, VQC) and feature mapping techniques (ZFeatureMap, ZZFeatureMap, and PauliFeatureMap). Results indicate that QSVC is notably robust under noise, whereas PegQSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise. The PauliFeatureMap is especially vulnerable, highlighting difficulties in maintaining accurate classification under noisy conditions. These findings underscore the critical importance of feature map selection and noise mitigation strategies in optimizing QML for genomic classification, with promising implications for personalized medicine.  \nIndex Terms—Quantum Noise, Feature Mapping, Quantum Support Vector Classifier, Pegasos QSVC, Variational Quantum Classifier, Quantum Neural Network  \nI. INTRODUCTION  \nTHE global quantum technology market is forecasted to  \nreach approximately USD 173 billion by 2040, with quantum computing projected to contribute between USD 28 billion and USD 72 billion by 2035, expanding further to USD 45 billion to USD 131 billion by 2040 . This projected growth underscores the importance of applications like Quantum Machine Learning (QML), which are expected to be significant drivers of early quantum computing value creation [1] .  \nIn this evolving landscape, QML stands out as a transformative intersection of quantum algorithms and classical machine learning, offering solutions to highly complex problems. Enabled by advances in quantum hardware and algorithms, QML has the potential to revolutionize fields such as data analysis, optimization, and predictive modeling [2], [3] . QML represents a near-term opportunity for quantum computers to showcase computational advantages over classical approaches, particularly through parameterized quantum circuits [4], enabling efficient processing of high-dimensional data that remains challenging for traditional methods [5] .  \nA promising application for QML is genomics, where the ability to analyze extensive genetic datasets is vital for understanding gene-disease relationships and advancing personalized treatments. Genomic data, characterized by its high dimensionality and complex structure, poses significant computational demands, often exceeding the capacity of classical algorithms. QML, leveraging the unique quantum properties  \nN. Singh and S. R. Pokhrel are with Deakin University, Geelong, VIC, Australia. Email: [n.navneetsingh@deakin.edu.au](n.navneetsingh@deakin.edu.au); [shiva.pokhrel@deakin.edu.au](shiva.pokhrel@deakin.edu.au).  \nof superposition and entanglement, can substantially improve data processing and pattern recognition, providing new insights into genetic diseases and enabling tailored therapies [6] .  \nHowever, the precision required for genomic analysis makes it particularly sensitive to quantum noise, which presents a major barrier to QML applications in this field [5] . Even minimal quantum noise in genomic classification tasks can lead to data misclassification, reducing the reliability of QML models for critical medical applications such as disease diagnosis and treatment development. Noisy Intermediate-Scale Quantum (NISQ) devices [7], [8], which offer near-term quantum capabilities [9], hold promise for revolutionizing genome sequencing and analysis through QML [10] . Despite their potential, these devices are highly susceptible to environmental interactions, leading to quantum noise that can disrupt and degrade QM","cbCaiqIWkjIPcAic","https://ap.wps.com/l/cbCaiqIWkjIPcAic","pdf",10423569,13,"English","# Introduction\n## Quantum technology market context and QML motivation\n## Genomics as a QML application and the role of dimensionality\n## Quantum noise as a barrier on NISQ hardware\n## Dataset realism and limitations of prior studies\n## Study objective, approach, and binary genomic classification setup","[{\"question\":\"Which quantum noise models are evaluated in the study?\",\"answer\":\"The study evaluates dephasing, amplitude damping, depolarizing, thermal noise, bit flip, and phase flip noise models and tests their effects on QML performance.\"},{\"question\":\"How do different QML algorithms respond to noise?\",\"answer\":\"QSVC is reported as notably robust under noise, while PegQSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise.\"},{\"question\":\"Why is feature map selection important under noisy conditions?\",\"answer\":\"The results indicate that feature maps can strongly affect classification accuracy under noise: PauliFeatureMap is especially vulnerable, demonstrating the difficulty of maintaining reliable learning without noise mitigation.\"}]","Modeling Feature Maps for Quantum Machine Learning | PDF",33]