Quantum State-Adaptive Machine Learning Dynamic Adjustment

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum state-adaptive machine learning is a rapidly evolving field with the potential to revolutionize artificial intelligence. This paper explores the design and implementation of a novel algorithm, Quantum State-Adaptive Machine Learning Dynamic Adjustment, which leverages quantum state dynamics to dynamically adjust model parameters and enhance model performance. The core mechanism centers around exploiting quantum superposition and entanglement to iteratively refine model parameters based on the inherent quantum properties of the training data. This approach offers a fundamentally new paradigm for machine learning, potentially surpassing the limitations of traditional algorithms. We present a theoretical framework and preliminary experimental results demonstrating the algorithm's effectiveness in optimizing a specific machine learning task, illustrating its potential to significantly improve model accuracy and generalization.

DOI: 10.5281/zenodo.22298907

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