  With the advent of noisy intermediate-scale quantum (NISQ) devices, practical
quantum computing has seemingly come into reach. However, to go beyond
proof-of-principle calculations, the current processing architectures will need
to scale up to larger quantum circuits which in turn will require fast and
scalable algorithms for quantum error correction. Here we present a neural
network based decoder that, for a family of stabilizer codes subject to
depolarizing noise, is scalable to tens of thousands of qubits (in contrast to
other recent machine learning inspired decoders) and exhibits faster decoding
times than the state-of-the-art union find decoder for a wide range of error
rates (down to 1%). The key innovation is to autodecode error syndromes on
small scales by shifting a preprocessing window over the underlying code, akin
to a convolutional neural network in pattern recognition approaches. We show
that such a preprocessing step allows to effectively reduce the error rate by
up to two orders of magnitude in practical applications and, by detecting
correlation effects, shifts the actual error threshold to $p_{\rm threshold} =
0.167(0)$, some ten percent higher than the threshold of conventional error
correction algorithms such as union find or minimum weight perfect matching. An
in-situ implementation of such machine learning-assisted quantum error
correction will be a decisive step to push the entanglement frontier beyond the
NISQ horizon.
