International Journal of Intelligent Systems and Applications in Engineering

Quantum Fusion: Enhancing Predictive Power through Entangled Machine Learning Ensembles

Smita Bhagwat, Pallavi Rege, Nitin Sakhare, Seema Babusing Rathod, Rupali Atul Mahajan, Archana Chougule,

Published Mar 16, 2024• 0 citations

Abstract

In the ever-evolving landscape of machine learning, this study explores the integration of quantum computing principles into predictive modelling through the novel concept of Quantum Fusion. By leveraging the unique properties of entanglement, Quantum Fusion enhances the predictive power of machine learning ensembles. The study demonstrates significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, when compared to traditional machine learning ensembles. The introduction of Quantum Entanglement emerges as a pivotal factor in achieving these advancements. The results not only underscore the superiority of Quantum Fusion but also contribute to the growing body of research on quantum-enhanced machine learning. As quantum computing continues to advance, the implications of Quantum Fusion have the potential to redefine the capabilities of predictive modelling, opening new frontiers in solving complex problems.

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