  We investigate the use of the evolutionary NEAT algorithm for the
optimization of a policy network that performs quantum error decoding on the
toric code, with bitflip and depolarizing noise, one qubit at a time. We find
that these NEAT-optimized network decoders have similar performance to
previously reported machine-learning based decoders, but use roughly three to
four orders of magnitude fewer parameters to do so.
