Quantum Entanglement Representation Learning Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel representation learning algorithm based on quantum entanglement. We explore the potential of leveraging quantum entanglement to enhance data analysis and feature extraction, particularly in the context of complex quantum state data. The core mechanism involves utilizing the unique properties of quantum entanglement to encode data and derive high-dimensional features, thereby improving the efficiency and accuracy of data processing. This algorithm aims to overcome limitations of traditional machine learning approaches when applied to quantum state data, presenting a promising avenue for advanced data analysis and feature learning. This work presents a preliminary investigation into the applicability of quantum entanglement for data representation, exploring potential benefits and challenges in this rapidly evolving field.

DOI: 10.5281/zenodo.22302665

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