[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123082-en":3,"doc-seo-123082-105":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":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},123082,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification","The study investigates Quantum Machine Learning (QML) methods for binary classification by comparing their performance against classical Machine Learning (ML) approaches. QML integrates principles from Quantum Computing (QC) and ML, targeting improved efficiency and potential quantum advantage for data-driven decision tasks. The work evaluates two QML models—Quantum Support Vector Classifier (QSVC) and Quantum Neural Networks (QNN)—using Qiskit across three datasets. Preprocessing includes PCA dimensionality reduction and standardization. Results indicate competitive accuracy versus classical baselines, with QSVC outperforming QNN, suggesting promise for scalable binary classification.","Article  \nImplementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification  \nSurajudeen Shina Ajibosin and Deniz Cetinkaya *  \nCitation: Ajibosin, S.S.; Cetinkaya, D. Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification. Software 2024, 3, 498–513. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)software3040024  \nAcademic Editor: Francisco José García-Peñalvo  \nReceived: 31 October 2024  \nRevised: 21 November 2024  \nAccepted: 26 November 2024  \nPublished: 28 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Computing & Informatics, Bournemouth University, Poole BH12 5BB, UK  \n* Correspondence: [dcetinkaya@bournemouth.ac.uk](dcetinkaya@bournemouth.ac.uk); Tel.: +44-1202-961241  \nAbstract: In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterpartsin terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.  \nKeywords: quantum machine learning; binary classification; quantum algorithms  \n1. Introduction  \nQuantum Computing (QC) exploits the principles of quantum mechanics to process information and solve problems that are too complex for classical computers. Unlike classical bits, qubits possess the unique ability to represent numerous possible combinations of zero and one at the same time. This simultaneous existence in multiple states is a phenomenon referred to as superposition. This property enables the processing of information in a parallel and exponentially expanded manner compared to classical computers. Although still in its early stages, quantum computing holds great promise for solving problems that are currently infeasible with classical computers [1] .  \nMachine Learning (ML) has significantly advanced human capabilities and contributed to societal progress across various fields by automating complex tasks, improving decisionmaking processes, and providing personalized experiences [2] . ML uses algorithms and statistical models to analyze and interpret complex data, assisting in planning, decision support, predictive analysis, intelligent automation, and many other activities across multiple domains including healthcare, finance, education, transportation, defense, etc. [3–5] .  \nML is a rapidly evolving and active field that is continuously b","cbCaibpX9bAhUvL2","https://ap.wps.com/l/cbCaibpX9bAhUvL2","pdf",3664321,1,16,"English","en",105,"# Introduction\n## Quantum computing background\n## Machine learning background\n## Motivation for QML\n# Methodology and Experimental Setup\n## QML algorithms evaluated\n## Datasets used\n## Preprocessing steps\n# Results and Performance Evaluation\n## Accuracy comparison with classical ML\n## QSVC vs QNN findings\n# Conclusion","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper focuses on binary classification, evaluating whether quantum machine learning can improve model performance on two-category prediction tasks.\"},{\"question\":\"Which QML algorithms are evaluated?\",\"answer\":\"The study examines Quantum Support Vector Classifier (QSVC) and Quantum Neural Networks (QNN) and compares them with classical machine learning methods.\"},{\"question\":\"How were the experiments conducted and what preprocessing was used?\",\"answer\":\"Experiments were implemented using Qiskit and run on three datasets. Data preprocessing included PCA-based dimensionality reduction and standardization using scalers.\"}]","Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification | PDF",1785814550,40,{"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},"implementation-and-performance-evaluation-of-quantum-machine-learning-algorithms-for-binary-classification","",{"@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/implementation-and-performance-evaluation-of-quantum-machine-learning-algorithms-for-binary-classification/123082/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper focuses on binary classification, evaluating whether quantum machine learning can improve model performance on two-category prediction tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which QML algorithms are evaluated?",{"text":80,"@type":76},"The study examines Quantum Support Vector Classifier (QSVC) and Quantum Neural Networks (QNN) and compares them with classical machine learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the experiments conducted and what preprocessing was used?",{"text":84,"@type":76},"Experiments were implemented using Qiskit and run on three datasets. 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