Quantum Machine Learning: Adaptive Loss Functions

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is an emerging field that explores the potential of quantum computing to revolutionize various computational tasks. This paper introduces a novel quantum machine learning framework leveraging adaptive loss functions to optimize quantum model parameters, ultimately aiming to improve model performance and reduce computational costs. The core mechanism involves dynamically adjusting the loss weight based on the model's error during training, providing a more sophisticated and adaptable approach compared to traditional, fixed loss functions. We analyze the benefits of this approach across different quantum model architectures and demonstrate its effectiveness through simulations and preliminary experiments. The paper concludes with a discussion of future research directions, focusing on expanding the applicability of this adaptive loss function to diverse quantum machine learning applications.

DOI: 10.5281/zenodo.22289728

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