基于量子态的自适应神经网络

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, termed "Quantum-Aware Adaptive Neural Network" (QANN), which leverages quantum computing principles to dynamically adjust network parameters during training. Traditional neural networks often struggle with optimal parameter settings, leading to suboptimal performance. QANN utilizes quantum circuits to represent and manipulate the input data, enabling a more adaptive and efficient training process. The core mechanism centers around quantum state manipulation to achieve enhanced generalization and predictive accuracy. We explore the potential of QANN to overcome these limitations and provide a promising approach to improve deep learning models. The paper details the design of the QANN architecture, the quantum algorithm employed, and the results obtained through simulations and preliminary experiments. Finally, we discuss the implications of this approach and future research directions.

DOI: 10.5281/zenodo.22269143

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