基于量子自联通的机器学习算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel machine learning algorithm, termed "Quantum Entanglement-Based Learning" (QEL), that leverages the unique properties of quantum entanglement to enhance the learning efficiency and generalization ability of machine learning models. Traditional machine learning approaches are inherently limited by classical computational constraints. Quantum entanglement offers a paradigm shift in computation, potentially enabling faster and more robust learning. We propose a QEL framework that utilizes entangled qubits to represent data and perform computations in a fundamentally different manner than conventional algorithms. This paper details the theoretical underpinnings, implementation details, and experimental results demonstrating the superiority of QEL across a range of benchmark datasets. The core mechanism of QEL relies on the ability of entangled qubits to exhibit correlated behavior, facilitating the exploration of complex data spaces and accelerating convergence toward optimal solutions. This work presents a significant advancement towards more efficient and accurate machine learning.

DOI: 10.5281/zenodo.22622399

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