Title: Quantum Feedback Loops in Neural Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum feedback loops offer a potentially transformative approach to neural network learning, addressing limitations in traditional methods through the integration of quantum phenomena. This paper explores the theoretical and practical implications of implementing quantum feedback loops within neural network architectures, aiming to accelerate learning speed and improve robustness in dynamic environments. We delve into the core mechanism of leveraging superposition and entanglement to enable faster adaptation and enhanced learning capabilities, focusing on the potential for novel optimization strategies. We conclude by highlighting the challenges and future directions for research in this rapidly evolving field.

DOI: 10.5281/zenodo.22291486

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