[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119574-en":3,"doc-seo-119574-105":30,"detail-sidebar-cat-0-en-105":90},{"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":4,"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},119574,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Quantum Machine Learning - Performance and Security Implications in Real-World Applications","Quantum computing creates a “quantum advantage” opportunity while simultaneously raising security and privacy risks. This poster analyzes performance and security implications of quantum machine learning (QML) using a real-world Alzheimer’s disease dataset. Quantum algorithms are compared with classical methods on learning ability and convergence, while also considering resource demands for simulation. Results suggest QML remains competitive yet does not clearly surpass classical learning, and it both inherits classical vulnerabilities and adds new attack vectors.","Quantum Machine Learning: Performance and Security Implications in Real-World Applications  \narXiv :2408 .04543v1 [ quant-ph] 8 Aug 2024  \nZhengping Jay Luo  \nDepartment of Computer Science and Physics Rider University Lawrenceville, New Jersey, 08648 Email: [zluo@rider.edu](zluo@rider.edu)  \nTyler Stewart  \nDepartment of Computer Science and Physics Rider University Lawrenceville, New Jersey, 08648 Email: [stewartty@rider.edu](stewartty@rider.edu)  \nMourya Narasareddygari  \nDepartment of Computer Science and Physics Rider University Lawrenceville, New Jersey, 08648 [Email: mnarasaredd@rider.edu](Email: mnarasaredd@rider.edu)  \nRui Duan  \nSchool of Science and Engineering University of Missouri-Kansas City Kansas City, Missouri, 64110 Email: [rdhk9@umkc.edu](rdhk9@umkc.edu)  \nShangqing Zhao  \nSchool of Computer Science University of Oklahoma Tulsa, Oklahoma, 74135 Email: [shangqing@ou.edu](shangqing@ou.edu)  \nAbstract—Quantum computing has garnered signi􀀂cant attention in recent years from both academia and industry due to its potential to achieve a ”quantum advantage” over classical computers. The advent of quantum computing introduces new challenges for security and privacy. This poster explores the performance and security implications of quantum computing through a case study of machine learning in a real-world application. We compare the performance of quantum machine learning (QML) algorithms to their classical counterparts using the Alzheimer’s disease dataset. Our results indicate that QML algorithms show promising potential while they still have not surpassed classical algorithms in terms of learning capability and convergence dif􀀂culty, and running quantum algorithms through simulations on classical computers requires signi􀀂cantly large memory space and CPU time. Our study also indicates that QMLs have inherited vulnerabilities from classical machine learning algorithms while also introduce new attack vectors.  \nIndex Terms—Quantum Security, Quantum Machine Learning, Quantum Advantage, Attack Vectors  \nI. INTRODUCTION  \nSince Richard Feynman 􀀂rst proposed the idea of harnessing quantum physics to build quantum computers more than 40 years ago [1], signi􀀂cant breakthroughs and progress have steadily been made toward realizing Feynman’s vision and achieving ”quantum advantage.” With the development of quantum computing technologies, a natural question to ask is how to protect the security and privacy in a quantum age? We conduct a case study regarding performance and security implications of machine learning (ML) algorithms in quantum computing. In recent years, numerous quantum machine learning (QML) proposals have been published [2]–[4], paving the way for unleashing the full potential of ML algorithms on quantum computers.  \nIn this poster, we want to know the performance of QML algorithms compared to their classical counterparts on a realworld dataset, and the corresponding security implications. We conduct a comparative study of the performance of  \ntwo major types of classical machine learning (CML) algorithms—support vector machines (SVMs) and multi-layer perceptron (MLP) classi􀀂ers—and their corresponding quantum versions, including quantum support vector machines (QSVMs), variational quantum algorithms (VQAs) and quantum convolutional neural networks (QCNNs) . Our comparison and analysis are based on the real-world Alzheimer’s disease dataset [5] . Then we’ll discuss the potential security implications of the QML algorithms, including the inherited vulnerabilities from their classical counterparts and the new introduced attack vectors.  \nII. QUANTUM MACHINE LEARNING  \nQML is the quantum counterpart to CML. In CML, there are two main categories: supervised and unsupervised learning. This poster focuses on supervised learning. Kernel methods, such as SVMs, and neural network-based methods, including MLPs and Convolutional Neural Networks (CNNs), are among the most renowned families of supervised learning algorit","cbCailLtdlTX8F7s","https://ap.wps.com/l/cbCailLtdlTX8F7s","pdf",70765,1,2,"English","en",105,"# Introduction\n## Quantum Machine Learning\n## Kernel Methods and QSVMs\n## Variational Quantum Algorithms\n## Quantum Convolutional Neural Networks","[{\"question\":\"How does the poster compare quantum machine learning performance with classical learning models?\",\"answer\":\"It compares QML algorithms with classical counterparts using the Alzheimer’s disease dataset, focusing on learning capability and convergence behavior.\"},{\"question\":\"Why are quantum machine learning security risks a concern in the quantum computing era?\",\"answer\":\"The poster highlights that quantum computing changes the security and privacy landscape, introducing new attack vectors while also inheriting weaknesses from classical machine learning.\"},{\"question\":\"What practical constraints affect running quantum algorithms in the study?\",\"answer\":\"It reports that simulations on classical computers require significantly large memory space and CPU time, even when evaluating quantum algorithms.\"}]","Quantum Machine Learning - Performance and Security Implications in Real-World Applications | PDF",1785725038,5,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"quantum-machine-learning-performance-and-security-implications-in-real-world-applications","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/quantum-machine-learning-performance-and-security-implications-in-real-world-applications/119574/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the poster compare quantum machine learning performance with classical learning models?","Question",{"text":74,"@type":75},"It compares QML algorithms with classical counterparts using the Alzheimer’s disease dataset, focusing on learning capability and convergence behavior.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why are quantum machine learning security risks a concern in the quantum computing era?",{"text":79,"@type":75},"The poster highlights that quantum computing changes the security and privacy landscape, introducing new attack vectors while also inheriting weaknesses from classical machine learning.",{"name":81,"@type":72,"acceptedAnswer":82},"What practical constraints affect running quantum algorithms in the study?",{"text":83,"@type":75},"It reports that simulations on classical computers require significantly large memory space and CPU time, even when evaluating quantum algorithms.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]