Performance of Various Low-level Decoder for Surface Codes in the Presence of Measurement Error

Quantum error correction is a research specialty within the area of quantum computing that constructs quantum circuits that correct for errors. Decoding is the process of using measurements from an error correcting code, known as error syndrome, to decide corrective operations to perform on the circuit. High-level decoding is the process of using the error syndrome to perform corrective logical operations, while low-level decoding uses the error syndrome to correct individual data qubits. Research on machine learning-based decoders is increasingly popular, but has not been thoroughly researched for low-level decoders. The type of error correcting code used is called surface code. A neural network-based decoder is developed and compared to a partial lookup table decoder and a graph algorithm-based decoder. The effects of increasing error correcting code size and increasing measurement errors on the error syndrome are analyzed for the decoders. The results demonstrate that there are advantages in terms of average execution time and resistance to increasing measurement error with the neural network-based decoder when compared to the two other decoders