Quantum Entanglement Path Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental phenomenon in quantum mechanics, holds immense promise for revolutionizing machine learning, particularly in the optimization of neural networks. This paper explores the potential of leveraging quantum entanglement to significantly enhance the training process of neural networks, aiming to reduce computational complexity and improve model quality. We propose a novel algorithm, Quantum Entanglement Path Optimization, that utilizes entangled qubits to dynamically adjust network parameters during training, fostering a more efficient and effective learning cycle. The core mechanism involves strategically manipulating the entanglement between network elements to guide the optimization process. This paper details the algorithm's design, mathematical framework, and preliminary experimental results, demonstrating a potential for substantial improvements in training speed and model performance. We conclude with a discussion of future research directions and the broader implications of this approach.

DOI: 10.5281/zenodo.22152373

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