Quantum machine learning ensembles: Harnessing entanglement for enhanced predictive power

Abstract

This study examines the combination of quantum computing and machine learning from an ensemble perspective, looking into how to introduce entanglement for improved predictive performance. The study presents new algorithms, Quantum Entangled Random Forest (QERF) and Boosting with Entanglement. They appear to outperform classical approaches in real-world issues. The theoretical establishments are laid for understanding quantum ensnarement in gatherings. Quantum machine learning gatherings are well adjusted to real-world applications, and empirical confirmations over numerous datasets drive this point domestically. The investigation looks at moral contemplations and stresses straightforward, capable quantum computing hones.