Topological Quantum Computing Based Adaptive Error-Correcting Code Generation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to error correction in topological quantum computing (TQC) leveraging its inherent fault-tolerance. The core idea centers on dynamically adapting the parameters of error-correcting codes based on real-time monitoring of error patterns within the quantum error correction (QEC) process. Traditional error correction schemes operate with fixed parameters, a limitation that becomes critical in the fluctuating environment of TQC. We propose a framework that utilizes machine learning algorithms to analyze the type and quantity of errors occurring during QEC, thereby enabling adaptive adjustment of code length and redundancy. This dynamic optimization promises to significantly improve the efficiency and reliability of QEC in TQC systems. The system is designed to respond to changing error profiles, maximizing correction success and minimizing resource overhead. This work highlights the potential of combining the robust nature of TQC with the intelligence of machine learning for advanced quantum error correction.

DOI: 10.5281/zenodo.22186819

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