Dynamic Quantum Error Correction Code Learning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum error correction (QEC) leveraging reinforcement learning (RL). The core idea is to develop an algorithm capable of dynamically learning and adapting QEC code parameters based on the evolving noise environment. Traditional QEC codes are often pre-defined and may not optimally perform in all scenarios. This research proposes a system where network nodes autonomously adjust the code parameters using an RL framework, reacting to error detection results and feedback signals. The system's ability to self-optimize based on real-time noise characteristics represents a significant advancement in QEC design. The key objective is to enhance error correction performance by adapting to the dynamic nature of quantum noise. The theoretical framework and the proposed algorithm are detailed within this document, highlighting the potential for improved resilience in quantum communication and computation.

DOI: 10.5281/zenodo.22447987

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