# Decoding surface code with a distributed neural network based decoder

## TL;DR

This paper introduces a distributed neural network decoder for surface codes that combines renormalization group techniques, improving scalability while maintaining high decoding performance under realistic noise conditions.

## Contribution

It proposes a novel distributed decoding algorithm merging RG methods with neural networks, addressing scalability issues in quantum error correction.

## Key findings

- Achieves comparable decoding performance to existing methods
- Addresses scalability limitations of neural network decoders
- Effective under depolarizing noise with noiseless syndrome measurements

## Abstract

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that, successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the Renormalization Group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the Blossom algorithm and a neural network based decoder. We show that similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders.