基于动态量子态的机器学习框架

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel machine learning framework based on dynamically adjusted quantum states. Traditional machine learning model parameter adjustment often relies on static adjustments, limiting the model's ability to adapt effectively. We propose a mechanism that leverages the inherent dynamic properties of quantum states to dynamically adjust the parameters of a machine learning model. This allows for more flexible and efficient training, potentially leading to improved performance across various machine learning tasks. The framework employs a quantum state representation to represent the input data and dynamically adjusts the model's parameters based on the quantum state's evolution. This approach offers a promising avenue for enhancing machine learning models by exploiting the unique characteristics of quantum systems.

DOI: 10.5281/zenodo.22193790

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