Quantum Machine Learning: Adaptive Optimization Algorithms

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is an emerging field that explores the potential of quantum computing to revolutionize various machine learning tasks. This paper investigates a novel adaptive optimization algorithm specifically tailored for QML, focusing on dynamically adjusting quantum parameter values to enhance model robustness and performance in the face of noisy training data. The core mechanism leverages the inherent self-adaptation capabilities of quantum machine learning to optimize the quantum system's parameters in response to the data distribution. We present a detailed analysis of the algorithm's design, its potential benefits, and its practical implications for advancing QML research.

DOI: 10.5281/zenodo.22189472

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