  The ultimate limits for the quantum machine learning of quantum data are
investigated by obtaining a generalisation of the celebrated No Free Lunch
(NFL) theorem. We find a lower bound on the quantum risk (the probability that
a trained hypothesis is incorrect when presented with a random input) of a
quantum learning algorithm trained via pairs of input and output states when
averaged over training pairs and unitaries. The bound is illustrated using a
recently introduced QNN architecture.
