Quantum Degradation Modeling and Adaptive Optimization for Error Correction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum error correction (QEC) that leverages quantum degradation modeling and adaptive optimization. Traditional QEC methods often employ fixed error correction codes, which may not be optimal for specific quantum hardware due to varying noise characteristics. Our approach addresses this limitation by dynamically adjusting the parameters of the QEC code based on a model of the degradation introduced by the quantum hardware. We establish a quantum degradation model, analyze the noise distribution using machine learning algorithms, and then optimize the parameters of the error correction code – such as code length and encoding rate – to achieve maximum quantum information transmission efficiency. The core contribution lies in the iterative feedback loop between the degradation model, the noise analysis, and the adaptive parameter adjustment, leading to a significantly improved QEC performance compared to static code implementations. This work offers a pathway towards more robust and efficient quantum communication and computation.

DOI: 10.5281/zenodo.22187606

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