Quantum Machine Learning via Entangled Feature Vectors

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum machine learning (QML) is a rapidly developing field that explores the potential of quantum computers to enhance machine learning algorithms. This paper investigates the application of entangled feature vectors to improve the performance of deep learning models. We propose a novel quantum algorithm that utilizes entanglement to generate correlated feature vectors, which are then used to augment the training data, resulting in improved accuracy and faster convergence. The core mechanism hinges on the unique properties of entanglement, which allows for enhanced feature representation and gradient propagation, thereby overcoming limitations imposed by classical data representation. We present a preliminary analysis demonstrating the potential of this approach, highlighting its benefits in scenarios where data scarcity is a significant constraint.

DOI: 10.5281/zenodo.22200023

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