Quantum Machine Learning: Parameter Optimization Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) holds immense potential for revolutionizing various fields, particularly machine learning. Traditional optimization methods often struggle with complex landscapes and computationally intensive tasks. This paper introduces a novel quantum machine learning parameter optimization framework leveraging quantum entanglement to dynamically adjust quantum neural network parameters, offering a significant improvement in learning efficiency and model accuracy. We propose a framework that utilizes quantum properties to reduce computational complexity and enhance the exploration of the parameter space. The core mechanism centers around harnessing quantum entanglement to facilitate rapid and nuanced parameter adjustments, ultimately leading to superior model performance. The paper details the design of this framework, outlines the theoretical underpinnings, and presents preliminary experimental results demonstrating its effectiveness.

DOI: 10.5281/zenodo.22183887

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