Quantum Hiding Matrix Adaptive Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Hiding Matrix Adaptive Optimization is a novel optimization framework designed to enhance the robustness and adaptability of machine learning models through the utilization of quantum mechanics. This approach leverages the unique properties of quantum hidden Markov models (HHMMs) – specifically, their inherent quantum entanglement – to dynamically adjust model parameters and mitigate overfitting. The core mechanism involves a quantum optimization process guided by entanglement, enabling the model to adapt to changing input data characteristics in real-time. This research explores the potential of quantum mechanics to revolutionize optimization algorithms, specifically for complex machine learning tasks. The paper presents a detailed mathematical framework and demonstrates its efficacy through simulations and preliminary experiments, showcasing significant improvements in model performance and generalization capabilities compared to conventional optimization techniques.

DOI: 10.5281/zenodo.22274134

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