Quantum Machine Learning: An Adaptive Loss Function Framework

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework leveraging adaptive loss functions to enhance model generalization. Traditional loss functions often suffer from fixed parameter settings, limiting their effectiveness in complex scenarios. Our approach dynamically adjusts the loss function parameters, reducing computational cost and improving model performance through an intelligent mechanism. We detail the framework's design, explain its core mechanism, and discuss its potential impact on quantum machine learning.

DOI: 10.5281/zenodo.22243963

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