[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118702-en":3,"doc-seo-118702-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118702,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Quantum Machine Learning Algorithms for Optimizing Complex Data Classification Tasks","Quantum Machine Learning (QML) integrates computational strengths of quantum computing with machine learning to tackle complex data classification. As data dimensionality grows and classical approaches encounter scalability limits, QML exploits superposition, entanglement, interference, and high-dimensional Hilbert-space representations to improve learning. The study reviews advanced algorithms, including QSVM, Variational Quantum Circuits, Quantum Neural Networks, and Quantum Kernel Estimation, evaluating binary and multi-class tasks on MNIST, the BCW dataset, and synthetic nonlinear data. Results report accuracy gains up to 96.8% alongside better decision margins and efficiency, while also noting constraints from noise, circuit depth, and hardware limitations.","Journal of Transactions in Systems Engineering  \n[https://journals.tultech.eu/index.php/jtse](https://journals.tultech.eu/index.php/jtse)  \nISSN: 2806-2973  \nVolume 4, Issue 1  \nDOI: [https://doi.org/10.15157/JTSE.2026.4.1.538-559](https://doi.org/10.15157/JTSE.2026.4.1.538-559)  \nReceived: 20.11.2025; Revised: 18.12.2025; Accepted: 31.12.2025  \nResearch Article  \nQuantum Machine Learning Algorithms for Optimizing Complex Data Classification Tasks  \nRamya Mandava1*, Gullapalli Lasya Sravanthi2  \n1*Georgia Institute of Technology, Atlanta, USA  \n2 Software Engineer, 18606 Alderwood Mall Pkwy, Unit 684, Lynnwood, Washington, USA.  \n*[ramyamresearcher@gmail.com](ramyamresearcher@gmail.com)  \nAbstract  \nQuantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN) . The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.  \nKeywords: Quantum Computing, Quantum Machine Learning, Variational Quantum Circuits, Quantum SVM, Quantum Neural Networks, Data Classification  \nINTRODUCTION  \nThe rapid increase in the number of data-intensive applications in various domains, including large-scale image analysis, has greatly changed the global computational ecosystem [1] . The high rate of digital information growth has resulted in an extremely complicated form of classification that standard machine learning models cannot manage because of the dimensionality of data that grows exponentially, non-linear interactions between features, and the requirements of real-time processing. Although they are strong, classical algorithms can be subjected to bottlenecks like inefficient computation, complexity of the model and extraction of complex patterns, which is experienced in highly variable datasets with entangled feature structure.  \nJournal of  \nTransactions in Systems Engineering  \n[https://doi.org/10.15157/JTSE.2026.4.1.538-559](https://doi.org/10.15157/JTSE.2026.4.1.538-559)  \n© 2024 Authors. This is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License CC BY 4.0 ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)).  \n 539  Quantum Machine Learning Algorithms for Optimizing Complex Data Classification Tasks  \nThese disadvantages highlight the dire need of more advanced means of calculations that may address the problems of the modern data spaces.  \nQuantum Machine Learning (QML) [2] is a new attempt to resolve these problems, which relies on the theory of quantum computations and machine learning algorithms.  \nThe QML relies on the key qua","cbCairvh95ojUad4","https://ap.wps.com/l/cbCairvh95ojUad4","pdf",1839172,1,22,"English","en",105,"# Introduction\n## Emergence of Quantum Computing and Challenges in Classical Classification","[{\"question\":\"What problem does Quantum Machine Learning (QML) aim to solve in data classification?\",\"answer\":\"QML targets complex classification tasks where classical models struggle due to rapidly increasing data dimensionality, nonlinear feature interactions, and real-time processing demands.\"},{\"question\":\"Which QML algorithms are reviewed for classification optimization?\",\"answer\":\"The paper reviews Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation for binary and multi-class classification.\"},{\"question\":\"How are the QML approaches evaluated and compared?\",\"answer\":\"They are evaluated on benchmark datasets such as MNIST and the Breast Cancer Wisconsin (BCW) dataset, along with synthetic nonlinear datasets, and compared against classical baselines including SVM, Random Forests, and Deep Neural Networks.\"}]","Quantum Machine Learning Algorithms for Optimizing Complex Data Classification Tasks | 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problem does Quantum Machine Learning (QML) aim to solve in data classification?","Question",{"text":76,"@type":77},"QML targets complex classification tasks where classical models struggle due to rapidly increasing data dimensionality, nonlinear feature interactions, and real-time processing demands.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which QML algorithms are reviewed for classification optimization?",{"text":81,"@type":77},"The paper reviews Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation for binary and multi-class classification.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the QML approaches evaluated and compared?",{"text":85,"@type":77},"They are evaluated on benchmark datasets such as MNIST and the Breast Cancer Wisconsin (BCW) dataset, along with synthetic nonlinear datasets, and compared against classical baselines including SVM, Random Forests, and Deep Neural 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