Quantum Error Correction Protocol Adaptive Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates adaptive optimization strategies for quantum error correction (QEC) protocols. The core claim is that dynamically adjusting QEC parameters based on real-time noise assessments can significantly improve correction efficiency. This is achieved through a mechanism utilizing quantum degradation models to monitor noise levels during the error correction process. Subsequently, machine learning algorithms are employed to autonomously optimize QEC parameters, such as encoding rate and redundancy, responding to the dynamic noise environment. The novelty lies in moving beyond fixed-parameter QEC schemes, enhancing robustness and adaptability across varying noise conditions. The proposed approach offers a pathway towards more efficient and reliable quantum information processing. ---

DOI: 10.5281/zenodo.22267779

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