Quantum Machine Learning Adaptive Regularization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that explores the potential of quantum computers to enhance machine learning algorithms. One promising approach is the utilization of adaptive regularization, which dynamically adjusts regularization parameters during training to improve model generalization. This paper introduces a novel framework for adaptive regularization, termed "Quantum Adaptive Regularization" (QAR), designed to automatically adjust regularization based on the inherent characteristics of the problem being addressed. We propose a mechanism that iteratively analyzes the data distribution and adjusts the regularization strength, leading to improved model performance and robustness. The core of this framework lies in a self-adaptive adjustment process, ensuring the model effectively adapts to the complexities of the training data. The potential of QAR to enhance the capabilities of QML is significant, offering a pathway towards more reliable and adaptable machine learning models. This work provides a foundational study of this innovative approach, demonstrating its effectiveness through theoretical analysis and preliminary experimental results.

DOI: 10.5281/zenodo.22243925

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