Machine Learning based Decoding of Heavy Hexagonal QECC for Asymmetric Quantum Noise

Abstract

Decoding error syndromes for topological quantum error correcting codes, such as surface and heavy hexagonal codes, is computationally expensive. While minimum weight perfect matching (MWPM) algorithms have been used, recent works have demonstrated the efficacy of machine learning (ML), particularly neural networks, in decoding syndromes for these codes. In this study, we introduce a ML-based decoder tailored to heavy hexagonal code to address asymmetric noise channels which reflect real-world scenario better than the depolarization model considered in previous works. Our proposed decoder shows ∼ 5× and ∼ 22× improvements in the threshold values for amplitude and amplitude-phase damping noise models respectively over MWPM methods. Our decoder is also robust to changes in asymmetry, with the threshold reducing by only ∼ 3.6% for a 10× change in asymmetry.