analiza aiqbench

A graph neural network decoder reduces logical error rates in quantum error correction by learning confidence metrics directly from error syndromes, bypassing the computationally intensive calculations required by traditional minimum-weight perfect matching decoders. This approach offers a scalable pathway for reliable quantum computation, particularly in scenarios where conventional gap estimates are inaccurate or impractical for specific noise models. The technique effectively replaces standard confidence measures with a learned proxy, enabling faster and more robust decoding without exhaustive comparisons of potential corrections.