Quantum Entanglement-Based Dynamic Parameter Adjustment

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement to dynamically adjust the parameters of a neural network, aiming to enhance its performance. We propose a novel algorithm that utilizes quantum entanglement to establish correlations between network parameters, leading to optimized behavior. This approach offers the potential for significantly improved learning speed and accuracy compared to traditional parameter tuning methods. The core mechanism involves creating entangled states between network parameters, enabling a more efficient and nuanced adjustment process. The study demonstrates the efficacy of this technique through simulation and preliminary experimental results, highlighting the potential for substantial gains in neural network performance.

DOI: 10.5281/zenodo.22154221

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