Kvantna korekcija napak z uporabo globokega učenja

Quantum computers hold promise for solving problems infeasible for classical machines. However, due to their susceptibility to errors, quantum error correction (QEC) is essential. This thesis investigates surface code decoders based on graph and recurrent neural networks, analyzing their predictions through soft outputs, uncertainty estimation, and hard case identification. For this purpose, we developed a pipeline for the analysis of models soft-outputs, revealing underlying properties of the data and behavior of models. We also established methods for detecting challenging syndromes and explored additional training on these cases. Furthermore, we also implemented a Bayesian approach to quantify uncertainty. Our results show that for lower code distances, decoders approximate maximum-likelihood decoding well, while the larger distance codes succumb to the sparsity of data. The hard case fine-tuning and the Bayesian approach did not significantly enhance performance of our models. Overall, our findings demonstrate the value of the analysis tools and training strategies developed in this work. These methods enabled us to better understand decoder behavior, identify sources of error, and outline concrete pathways for improving quantum error correction in future studies.