Quantum Entanglement-Based Quantum Machine Learning Model Prediction

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental phenomenon in quantum mechanics, offers unique properties that can be harnessed for enhanced computational capabilities. This paper explores the application of quantum entanglement to accelerate and improve quantum machine learning models, specifically focusing on a novel prediction framework. The core claim centers on leveraging quantum entanglement and self-consistency to achieve superior prediction accuracy and efficiency compared to classical machine learning techniques. We propose a framework that utilizes entangled qubits to create a correlated state, allowing for the extraction of subtle correlations within data that classical models might miss. This leads to more robust and accurate predictions. The paper details the design of this framework, its mathematical underpinnings, and preliminary experimental results demonstrating its potential for significant advancements in predictive accuracy.

DOI: 10.5281/zenodo.22511466

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