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

Understand

Scaling quantum computing to practical applications necessitates reliable quantum error correction.

• Although numerous correction codes have been proposed, the overall correction efficiency critically limited by the decode algorithms.
• We introduce GraphQEC, a code-agnostic decoder leveraging machine-learning on the graph structure of stabilizer codes with linear time complexity.
• GraphQEC demonstrates unprecedented accuracy and efficiency across all tested code families, including surface codes, color codes, and quantum low-density parity-check (QLDPC) codes.