Self-Adaptive Quantum State for Quantum Machine Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on self-adaptive quantum states. Traditional quantum machine learning algorithms often rely on computationally expensive and static parameter adjustments, limiting their potential for optimization. We propose a system that dynamically adjusts quantum states using quantum entanglement and superposition, enabling automated optimization and enhanced efficiency and accuracy. This approach leverages the unique properties of these quantum phenomena to overcome limitations of current quantum algorithms. The core mechanism focuses on using controlled entanglement to influence the quantum state, leading to improved performance in various machine learning tasks. We demonstrate the efficacy of this framework through a series of experiments evaluating its performance on a range of quantum machine learning algorithms, showcasing significant improvements in both training speed and prediction accuracy compared to classical machine learning methods. The findings highlight the potential of this self-adaptive approach to revolutionize quantum machine learning.

DOI: 10.5281/zenodo.22253449

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