[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290900-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290900-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","modeling-quantum-machine-learning-for-genomic-data-analysis","Modeling Quantum Machine Learning for Genomic Data Analysis","","Quantum Machine Learning (QML) evolves to enable new application opportunities, and this study evaluates QML models for binary classification of genome sequence data. Multiple feature mapping techniques are used, with an open-source Qiskit-based implementation to run experiments on a benchmark genomic dataset. Results show performance depends strongly on the pairing between feature maps and QML algorithms: Pegasos-QSVC is especially recall-oriented, while QNN achieves the highest training accuracy. Variation across mappings also signals potential overfitting to localized output distributions.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/modeling-quantum-machine-learning-for-genomic-data-analysis/290900/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/modeling-quantum-machine-learning-for-genomic-data-analysis/290900.png","ImageObject",300,407,{"name":42,"@type":43},"Eden","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the document’s main goal for quantum machine learning in genomics?","Question",{"text":63,"@type":64},"To investigate and evaluate how QML models can perform binary classification of genome sequence data, including the effect of feature mapping techniques on overall performance.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which models are specifically highlighted in the study’s experiments?",{"text":68,"@type":64},"QSVC, Pegasos-QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN), evaluated across different feature mapping approaches.",{"name":70,"@type":61,"acceptedAnswer":71},"What key factor affects QML classifier performance according to the simulations?",{"text":72,"@type":64},"The interplay between feature mapping techniques and the chosen QML algorithm, which can also lead to variability and risk of overfitting in some scenarios.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290900,1789642919,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Modeling Quantum Machine Learning for Genomic  \nData Analysis  \nNavneet Singh and Shiva Raj Pokhrel, IEEE, Senior Member  \narXiv :2501 .08193v1 [ cs .LG] 14 Jan 2025  \nAbstract—Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome sequence data by employing various feature mapping techniques. We present an open-source, independent Qiskit-based implementation to conduct experiments on a benchmark genomic dataset. Our simulations reveal that the interplay between feature mapping techniques and QML algorithms significantly influences performance. Notably, the Pegasos Quantum Support Vector Classifier (Pegasos-QSVC) exhibits high sensitivity, particularly excelling in recall metrics, while Quantum Neural Networks (QNN) achieve the highest training accuracy across all feature maps. However, the pronounced variability in classifier performance, dependent on feature mapping, highlights the risk of overfitting to localized output distributions in certain scenarios. This work underscores the transformative potential of QML for genomic data classification while emphasizing the need for continued advancements to enhance the robustness and accuracy of these methodologies.  \nIndex Terms—Feature Map, Genomic Sequence Classification, Pegasos-QSVC, Quantum Machine Learning, Quantum Neural Networks (QNN), Quantum Support Vector Classifier (QSVC), Variational Quantum Circuits (VQC)  \nI. INTRODUCTION  \nThe development of Noisy Intermediate-Scale Quantum (NISQ) devices, harnessing quantum information and physics, marks an epoch in computing [1] . These quantum devices promise applications beyond classical computers, positioning quantum machine learning (QML) as a key research area [2] . This drive, fueled by the exponential power of quantum machines and advances in QML, radically alters large scale data processing. In recent years, QML have shown multiple use-cases in different domains such finance, astronomy, automobile and healthcare. However, finding practically important problems for quantum hardware and algorithms to work remains challenging [3] . As noted in [4], integrating QML into healthcare can revolutionize genomic analysis by leveraging the computational power of quantum algorithms. In genomics, classical ML techniques have led to significant advancements in understanding genetic disorders, evolutionary biology, and personalized medicine by handling large, complex datasets [5] . However, ML methods struggle to handle the high-dimensional, complex nature of genomic data, typically necessitating sophisticated models that are computationally expensive and lack scalability [6]–[8] .  \nIn this paper, we advocate that QML approaches enable the utilization of quantum computing power to analyze complex  \nN. Singh and S. R. Pokhrel are with Deakin University, Geelong, VIC, [Australia. Email: n.navneetsingh@deakin.edu.au](Australia. Email: n.navneetsingh@deakin.edu.au); [shiva.pokhrel@deakin.edu.au](shiva.pokhrel@deakin.edu.au).  \ngenomic data at a scale more efficiently than that of ML [9],[10] . We seek to develop a framework to serve genome classification well, where genomic sequences are analyzed to identify genetic variation (function), evolutionary relationships, and disease associations. Such a genome classification is very important for the diagnosis and treatment of genetic disorders, evolutionary biology studies, and personalized medicine. We explain and demonstrate how QML is poised to help us overcome the challenges of increasingly available genomic data based on a complex structure on a large scale while maintaining a fast computational classification algorithm [11],[12] .  \nDespite significant advances in QML for medical imaging analysis, genomic sequence classification remains an underexplored frontier. Our research addresses this gap by critically assessing, expanding,","cbCaigGUG9jSh8k7","https://ap.wps.com/l/cbCaigGUG9jSh8k7","pdf",788259,11,"English","# Introduction\n## Motivating Research Questions\n## Our Proposed Solution Approach and Contribution","[{\"question\":\"What is the document’s main goal for quantum machine learning in genomics?\",\"answer\":\"To investigate and evaluate how QML models can perform binary classification of genome sequence data, including the effect of feature mapping techniques on overall performance.\"},{\"question\":\"Which models are specifically highlighted in the study’s experiments?\",\"answer\":\"QSVC, Pegasos-QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN), evaluated across different feature mapping approaches.\"},{\"question\":\"What key factor affects QML classifier performance according to the simulations?\",\"answer\":\"The interplay between feature mapping techniques and the chosen QML algorithm, which can also lead to variability and risk of overfitting in some scenarios.\"}]","Modeling Quantum Machine Learning for Genomic Data Analysis | PDF",28]