基于量子态的非线性模型参数调整

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel approach to model parameter adjustment for non-linear quantum state models. Traditional parameter adjustment methods often rely on iterative optimization algorithms, which can be computationally expensive and may not always converge to optimal solutions. We propose a new framework that leverages quantum state entanglement to dynamically adjust model parameters, enhancing training efficiency and potentially improving model performance. This approach utilizes a quantum state representation to guide parameter adjustments, offering a fundamentally different perspective on parameter optimization. The paper details the theoretical underpinnings of the quantum state entanglement mechanism and demonstrates its effectiveness through simulations and initial experiments. We highlight the potential advantages of this method over existing techniques, emphasizing its capacity to address challenges related to convergence and sensitivity to initial conditions.

DOI: 10.5281/zenodo.22157995

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