Data-Driven Decoding of Quantum Surface Codes using Recurrent Neural Networks

Quantum computers has the potential to outperform classical computers for certain tasks, but the practical use of quantum computers is limited by noise and errors in quantum systems. Quantum Error Correction (QEC) provides a way for detecting and correcting these errors, but in the presence of complex and correlated noise, efficient decoding still remains a significant challenge. In this thesis, neural network-based decoders are explored as an approach to decode QEC data. In particular, recurrent neural network (RNN) architectures, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are investigated as their are able to capture temporal dependencies and model sequential data. GRU and LSTM models are trained using the data generated with the help of the Stim simulator for quantum circuits that are based on experimental setups. The models are trained to classify error occurrences from detection event data and evaluated under different configurations, including varying number of rounds, code distance and input representation. The results show that both GRU and LSTM models can learn error patterns for shorter sequences and provide competitive decoding performance. However, for longer sequences the models struggle. Overall, this work demonstrates that relatively simple neural network models can be used as alternatives to traditional decoding methods and highlights the potential of machine learning approaches for improving quantum error correction.

Author dc:creator

• Kristófer Darri Finnsson 1997-

Contributors dc:contributor

• Háskóli Íslands

• Tölvunarfræði

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/1946/53182

OAI identifier oai:identifier

oai:skemman.is:1946/53182