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Quantum Machine Learning: Adaptive Noise Injection for Enhanced Generalization and Robustness

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that explores the potential of quantum algorithms to revolutionize machine learning tasks. A critical challenge in QML is the need for robust models that can generalize well to unseen data and exhibit resilience to perturbations. This paper introduces a novel QML model, Quantum Machine Learning with Adaptive Noise Injection (QML-ALI), designed to address this challenge by incorporating a dynamic noise injection mechanism. We propose a mechanism that intelligently adjusts the amount of noise injected during training based on the observed training dynamics, ultimately promoting improved generalization and robustness. This approach contrasts with traditional methods that rely on fixed noise levels, offering a more adaptable and potentially superior solution. The core claims and mechanisms are detailed, and the paper concludes with a discussion of the potential implications and future directions of this research.

DOI
10.5281/zenodo.22451665

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