  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 and syndrome measurement errors, 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, up to fifteen percent higher than the threshold of
conventional error correction algorithms such as union find or minimum weight
perfect matching, even in the presence of measurement errors. 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.
