Quantum Machine Learning: Adaptive Loss Function Design

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in computational intelligence, leveraging the unique properties of quantum mechanics to solve complex problems currently intractable for classical computers. This paper explores the design of a novel quantum machine learning algorithm incorporating dynamic adjustment of the loss function based on quantum state evolution. We propose a quantum loss function that adapts to the learning process, potentially enhancing learning efficiency and generalization capabilities. The core mechanism centers around exploiting quantum entanglement to dynamically modify the loss function, offering a potential breakthrough in QML performance. The study details the algorithm's architecture, the role of quantum entanglement in the loss function adjustment, and the expected benefits in terms of training speed and accuracy.

DOI: 10.5281/zenodo.22224880

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