基于量子纠缠的自适应参数调整

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of quantum entanglement to revolutionize parameter adjustment in machine learning models. Traditional parameter adjustment methods often rely on iterative refinement, which can be slow and computationally expensive. We propose a novel approach leveraging quantum entanglement to dynamically adjust parameters during the training process, potentially accelerating learning and improving model performance. This work investigates the feasibility of using entangled states to facilitate faster and more efficient search, offering a fundamentally different paradigm for parameter optimization. We outline the theoretical framework, describe the implementation of the quantum entanglement-based mechanism, and present preliminary results demonstrating a significant improvement in learning speed compared to classical methods. The core claim is that this approach, leveraging quantum entanglement, provides a new and potentially transformative mechanism for parameter optimization, offering a pathway towards more sophisticated and efficient machine learning models.

DOI: 10.5281/zenodo.22186408

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