Quantum Machine Learning: Probability Structure Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) holds significant promise for advancing artificial intelligence, leveraging the unique capabilities of quantum mechanics. A crucial aspect of QML is the optimization of quantum machine learning algorithms, and this paper proposes a novel approach centered around the integration of probability structure optimization. We argue that by strategically employing probability structure optimization, we can enhance the robustness and generalization capabilities of quantum machine learning models, particularly in the presence of noise. This research explores a specific mechanism involving the combined application of quantum machine learning algorithms and probabilistic structure optimization to improve the overall performance of quantum machine learning systems. The core claim is that this integration yields a substantial improvement in model accuracy and resilience against noise, representing a significant step towards practical QML applications.

DOI: 10.5281/zenodo.22250033

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