基于动态量子态的神经网络

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of utilizing dynamic quantum states to enhance neural network performance, offering a novel approach to data representation and modeling. We propose a framework that leverages the inherent adaptability of quantum states to dynamically adjust the input data distribution, leading to more precise and robust models. The core mechanism involves simulating data evolution through quantum transformations, and subsequently mapping this evolution to a vector representation via a neural network. This approach aims to overcome limitations of traditional neural network architectures, potentially yielding improved accuracy and generalization capabilities. The study examines the theoretical advantages, potential challenges, and preliminary experimental results demonstrating the efficacy of this dynamic quantum state approach.

DOI: 10.5281/zenodo.22195540

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