Quantum Machine Learning: A Novel Non-Linear Adaptive Loss Function

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that leverages the unique properties of quantum mechanics to develop algorithms and computational models. This paper explores the application of QML to machine learning, specifically focusing on the design and implementation of a novel non-linear adaptive loss function. The core challenge lies in effectively modeling complex, non-linear relationships within datasets, and traditional approaches often struggle with this task. This research proposes a framework utilizing quantum algorithms to dynamically adjust the loss function during training, allowing the model to better capture intricate dependencies. We demonstrate the effectiveness of this approach through theoretical analysis and preliminary experimental results, highlighting its potential to improve model performance and generalization capabilities. The focus is on creating a loss function that is inherently more adaptable and capable of handling intricate data patterns.

DOI: 10.5281/zenodo.22516348

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