  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.
