[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121423-en":3,"doc-seo-121423-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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},121423,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Quantum Computing in Artificial Intelligence - A Review of Quantum Machine Learning Algorithms","Quantum computing and artificial intelligence converge to form Quantum Machine Learning (QML), which strengthens classical machine learning by using quantum devices’ computational advantages. The review surveys advanced QML algorithms, including QSVMs, QkNN, QPCA, QNNs, and QRL, summarizing their theoretical and practical status and reported empirical performance. Results indicate potential speed-ups in classification, clustering, and optimisation, especially for ideal quantum systems, while hardware limits, software irregularities, and training challenges such as barren plateaus constrain real-world impact.","Quantum Computing in Artificial Intelligence:  \na Review of Quantum Machine Learning Algorithms  \nOlolade Funke Olaitan 1, Samuel Oluwabukunmi Ayeni 2, Adedapo Olosunde 3,  \nFrancis Chukwudalu Okeke 4, Ugochukwu Udonna Okonkwo 5, Chukwuemeka George Ochieze 6, Osinachi Victor Chukwujama 7, Ogheneruemu Nathaniel Akatakpo 8  \n1 The University of Utah  \n201 Presidents Circle, Salt Lake City, UT 84112, USA  \n2 Oregon State University  \n1500 SW Jefferson Way, Corvallis, OR 97331, USA  \n3 The University of Toledo  \n2801 Bancroft St, Toledo, OH 43606, USA  \n4 C. K. Tedam University of Technology and Applied Sciences  \nBox 24 Kassena-Nankana Navrongo-Kolgo Road, Navrongo, Upper East, Ghana  \n5 Southern Illinois University, Edwardsville  \nCampus Box 1015, Edwardsville, Illinois, 62026-1015, USA  \n6 The University of Virginia  \nP.O. Box 400132, Charlottesville, VA 22904-4132, USA  \n7 Federal University of Technology Owerri  \nP. M. B, 1526 Owerri, Ihiagwa, Nigeria  \n8 University of Benin  \nP. M. B. 1154, Ugbowo, Benin City, Edo State, Nigeria  \nDOI: 10.22178/pos.117-25  \nLCC Subject Category: T1-995  \nReceived 26.04.2025 Accepted 25.05.2025 Published online 31.05.2025  \nCorresponding Author: Ugochukwu Udonna Okonkwo  \n[ugochukwu.udonna.okonkwo@gmail.com](ugochukwu.udonna.okonkwo@gmail.com)  \n© 2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License   \nAbstract. Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, environmental sustainability, urban planning methodology, drug discovery, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.  \nKeywords: Artificial Intelligence, Machine Learning, Quantum Algorithms, Quantum Computing, Quantum Machine Learning  \nINTRODUCTION  \nArtificial intelligence (AI) has become one of the most transformative technologies of the 21st century, essentially opening up new possibilities for machines to accomplish tasks that would previously require human intelligence. From speech recognition to self-driving cars and healthcare diagnosis, AI has a broad range of applications. At the heart of AI is the ability to use machine learning (ML) to recognise patterns in data, make predictions, and improve over time. However, with the explosive accumulation of data and increasing complexity, the use of classical ML models often presents high computational loads, necessitating the need for potent computational resources and extended training times [1].  \nWith the progress made in AI, Quantum Computing (QC) is emerging as a new paradigm for information processing. Rather than t","cbCair5xT4NWOoY7","https://ap.wps.com/l/cbCair5xT4NWOoY7","pdf",452432,1,9,"English","en",105,"# Introduction\n## Motivation: AI challenges in classical ML\n## Quantum computing fundamentals and qubits\n## Emergence and goals of Quantum Machine Learning (QML)\n## Objectives of the review","[{\"question\":\"What is Quantum Machine Learning (QML) and why is it important?\",\"answer\":\"QML is a discipline that enhances classical machine learning by leveraging quantum mechanics. It aims to address limitations of classical models such as speed, scalability, and handling high-dimensional data.\"},{\"question\":\"Which Quantum Machine Learning algorithms are reviewed in the paper?\",\"answer\":\"The review covers Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL).\"},{\"question\":\"What factors limit the practical usefulness of QML according to the findings?\",\"answer\":\"Hardware constraints, software irregularities, and training issues such as barren plateaus limit current practical utility. Many applications are still at proof-of-concept level.\"}]","Quantum Computing in Artificial Intelligence - A Review of Quantum Machine Learning Algorithms | PDF",1785735597,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-computing-in-artificial-intelligence-a-review-of-quantum-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantum-computing-in-artificial-intelligence-a-review-of-quantum-machine-learning-algorithms/121423/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Quantum Machine Learning (QML) and why is it important?","Question",{"text":75,"@type":76},"QML is a discipline that enhances classical machine learning by leveraging quantum mechanics. It aims to address limitations of classical models such as speed, scalability, and handling high-dimensional data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which Quantum Machine Learning algorithms are reviewed in the paper?",{"text":80,"@type":76},"The review covers Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL).",{"name":82,"@type":73,"acceptedAnswer":83},"What factors limit the practical usefulness of QML according to the findings?",{"text":84,"@type":76},"Hardware constraints, software irregularities, and training issues such as barren plateaus limit current practical utility. 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