Quantum Machine Learning Models for Enhancing Big Data Analytics
subject: Big Data Analytics; High-Dimensional Data; Hybrid Quantum-Classical Models; Pattern Recognition; Predictive Analytics; Quantum Computing; Quantum Machine Learning; Real-Time Decision Making
description: 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.
creator: Prasant, P.; Hariharan, R.; Sudha, E.; Jeshurun, Subramania Bala; Vedapradha, R.; Reddy, Paindla Vamshi Vardhan
source: 2025 International Conference on Future Technologies, ICFT 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833156815-3;
language: English
type: Conference paper
coverage: Prasant P., SRM University, Department of Computer Science & 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