Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correction

Quantum error correction is crucial for large-scale quantum computing, but the absence of efficient decoders for new codes like quantum low-density parity-check (QLDPC) codes has hindered progress. Here we introduce a universal decoder based on linear attention sequence modeling and graph neural network that operates directly on any stabilizer code's graph structure. Our numerical experiments demonstrate that this decoder outperforms specialized algorithms in both accuracy and speed across diverse stabilizer codes, including surface codes, color codes, and QLDPC codes. The decoder maintains linear time scaling with syndrome measurements and requires no structural modifications between different codes. For the Bivariate Bicycle code with distance 12, our approach achieves a 39.4% lower logical error rate than previous best decoders while requiring only ~1% of the decoding time. These results provide a practical, universal solution for quantum error correction, eliminating the need for code-specific decoders.