Efficient Syndrome Decoding for Heavy Hexagonal Quantum Error Correcting Codes using Machine Learning

This work proposes an efficient machine learning-based syndrome decoder for heavy hexagonal quantum error correcting codes, which achieves significantly higher threshold and pseudo-threshold values compared to the state-of-the-art minimum weight perfect matching decoder.