Quantum Machine Learning in Noise Matrix Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in machine learning, potentially surpassing classical approaches due to quantum phenomena. A critical challenge in many QML applications is the need to effectively manage noise within the model parameters, particularly in training datasets with high dimensionality. This paper explores the application of a novel noise matrix optimization algorithm, specifically tailored for QML, leveraging quantum properties to enhance model accuracy and stability. We propose a method that utilizes quantum superposition and entanglement to explore the parameter space more efficiently, leading to improved generalization performance. The core mechanism focuses on manipulating the noise matrix through quantum rotations and entanglement to achieve a more nuanced and robust optimization. We demonstrate the effectiveness of this approach through a series of simulations and preliminary experiments, highlighting its potential to significantly improve model performance in specific scenarios. The paper concludes with a discussion of future research directions and the implications of this quantum optimization technique for the field of machine learning.

DOI: 10.5281/zenodo.22296433

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