基于量子神经网络的自动微分算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel automatic differentiation algorithm based on quantum neural networks (QNNs). Traditional automatic differentiation methods often rely on manual adjustment of parameters, lacking inherent adaptability. This work proposes a new approach utilizing the unique properties of QNNs to automate gradient calculation, ultimately aiming to enhance the training efficiency of neural networks. The core mechanism involves incorporating quantum circuits within the network architecture to enable efficient and adaptive gradient computation. We detail the algorithm's design, explain the underlying quantum principles employed, and present preliminary experimental results demonstrating its improved performance compared to existing methods.

DOI: 10.5281/zenodo.22325928

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