Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prasant, P.; Hariharan, R.; Sudha, E.; Jeshurun, Subramania Bala; Vedapradha, R.; Reddy, Paindla Vamshi Vardhan Title A name given to the resource Quantum Machine Learning Models for Enhancing Big Data Analytics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Future Technologies, ICFT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICFT66708.2025.11336505" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICFT66708.2025.11336505</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033653203?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033653203?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prasant P., SRM University, Department of Computer Science &amp; Engineering, U.P., Ghaziabad, India; Hariharan R., International Institute of Business Studies, Department of Management, Bangalore, India; Sudha E., CHRIST (Deemed to Be University), Department of Commerce, Bangalore, India; Jeshurun S.B., School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India; Vedapradha R., School of Commerce, St. Joseph's College of Commerce, Bangalore, India; Reddy P.V.V., Vidya Jyothi Institute of Technology, Hyderabad, India Description An account of the resource The blistering growth of information in contemporary business is a great challenge to the traditional analytics in the context of speed, accuracy, and scalability. The Quantum Machine Learning (QML) has the chance to provide a ground-breaking solution, based on quantum superposition, entanglement execution to speed up a computational process and increase predictability. The current work proposed a Hybrid Quantum Classical Framework (HQCF) which is a combination of quantum algorithms and conventional machine learning to solve high-dimensional big data analytics. The proposed system shows huge performance improvements over classical foundations - attaining up to 10-percent higher prediction error, and cutting training costs by a factor of 47 percent in various fields such as finance, healthcare, and internet of things sensor information. The hybrid structure features high scalability as well which means that it can process datasets that are up to six million samples and thus it has a high level of scalability and strength. Such quantitative indicators indicate that quantum-enhanced analytic is technologically advanced or progressive to enhance computational-efficiency, generalization-of-models, and real-time ability to make a decision, with large-scale data settings. 2025 IEEE. Subject The topic of the resource Big Data Analytics; High-Dimensional Data; Hybrid Quantum-Classical Models; Pattern Recognition; Predictive Analytics; Quantum Computing; Quantum Machine Learning; Real-Time Decision Making Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156815-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online