基于自适应学习的参数化量子纠错

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a cornerstone of quantum technology, is highly susceptible to noise and errors, posing significant challenges in quantum computation and communication. Quantum error correction (QEC) relies heavily on the precise calibration and adjustment of error correction codes, a task that is often computationally intensive and requires significant effort. This paper proposes a novel approach to QEC – a system of parameterized quantum error correction – leveraging the power of adaptive learning. We introduce a dynamic parameterization scheme that continuously optimizes the error correction process, improving both the accuracy and efficiency of the system. Our work demonstrates the potential of this approach to significantly enhance QEC performance through automated parameter adjustment and reduced computational overhead. The core mechanism centers around a self-learning algorithm that adapts the error correction parameters based on the observed error rates and fidelity, ultimately leading to a more robust and efficient error correction protocol.

DOI: 10.5281/zenodo.22192654

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