基于量子态的神经网络的自适应自校正机制

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, termed "Quantum-Adaptive Neural Network" (QANN), that leverages the unique properties of quantum states for self-correction and stable learning. Traditional neural networks often rely on static parameter adjustments, limiting their adaptability and stability. The QANN employs a dynamic mechanism based on quantum state evolution to proactively address and mitigate errors, achieving significantly enhanced learning performance and robustness. The core of this design lies in the utilization of quantum state self-entanglement to dynamically adjust network parameters, offering a fundamentally different approach to neural network training. The proposed method addresses the limitations of current techniques by providing a mechanism for continuous error correction, enhancing both the learning process and the network's overall resilience. The paper details the mathematical framework, experimental validation, and preliminary analysis of the QANN, demonstrating its potential for improved machine learning capabilities.

DOI: 10.5281/zenodo.22509521

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