Quantum Machine Learning: Non-Linear Function Design

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in computational science, leveraging the unique properties of quantum mechanics to develop algorithms with enhanced capabilities. A critical aspect of QML is the design of effective quantum algorithms, and a significant challenge lies in effectively utilizing quantum systems to achieve desired outcomes. This paper explores the application of non-linear function design as a core mechanism for accelerating and improving quantum machine learning. We propose a novel approach that leverages quantum entanglement and the dynamic nature of quantum states to generate and optimize non-linear functions crucial for QML, aiming to overcome limitations inherent in traditional approaches. This research will focus on the mathematical formulation and implementation of such functions, demonstrating their potential to unlock new quantum machine learning algorithms.

DOI: 10.5281/zenodo.22592659

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