Quantum Neural Networks with Dynamic Decoherence Suppression Mechanism

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper presents a novel approach to enhancing the performance of Quantum Neural Networks (QNNs) through a dynamic decoherence suppression mechanism. Traditional decoherence mitigation strategies are often static and fail to adapt to the inherent fluctuations and noise present in quantum systems. We propose a feedback-based system that continuously monitors and corrects decoherence within the QNN, leveraging quantum measurement and control. The core claim of this work is to design a quantum feedback-based dynamic decoherence suppression mechanism to real-time monitor and correct decoherence in QNNs, thereby improving the network's computational capabilities and stability. The proposed mechanism relies on the core mechanism of utilizing quantum measurement and control to monitor and compensate for the decoherence effects, maintaining coherence between qubits. This dynamic approach addresses the limitations of static methods, offering a more robust and adaptable solution for QNNs. Mathematical formulations detailing the system dynamics and control strategies are presented, alongside a discussion of the theoretical underpinnings and potential applications. The effectiveness of the proposed mechanism is conceptually outlined, highlighting its ability to significantly improve the resilience of QNNs against environmental noise and ultimately boosting their computational performance. ---

DOI: 10.5281/zenodo.22245495

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