Adaptive Loss Function Optimization for Quantum Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize various fields, including pattern recognition, optimization, and data analysis. However, the inherent complexity of QML algorithms necessitates innovative approaches to optimization. This paper proposes an adaptive loss function optimization method based on quantum machine learning, which dynamically adjusts the weights and parameters of the loss function based on the input data's characteristics. This dynamic adjustment is designed to enhance model performance and generalization capabilities. The core mechanism leverages quantum properties to provide a flexible and adaptive optimization process. We present a theoretical framework outlining the method's operation, discussing its potential benefits and limitations, and outlining future research directions.

DOI: 10.5281/zenodo.22297430

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