Title: Adaptive Quantum Error Correction with Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum information processing offers unparalleled computational power, but the inherent fragility of quantum systems necessitates robust error correction. Traditional error correction codes struggle to effectively handle complex error profiles, hindering the realization of practical quantum technologies. This paper proposes an adaptive quantum error correction scheme leveraging neural networks to dynamically adjust error correction operations based on the observed error characteristics of a quantum system. We detail a novel training methodology for a neural network trained to predict and mitigate errors, resulting in improved error mitigation compared to existing approaches. The core mechanism involves a continuous feedback loop, refining the neural network's error correction strategy in real-time, optimizing for both error correction and system fidelity. The potential benefits of this approach include increased error correction rates, reduced computational overhead, and improved overall system robustness. This research explores the practical implications of this adaptive scheme through simulations and preliminary experimental validations.

DOI: 10.5281/zenodo.22195034

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