  We study quantum learning algorithms for quantum measurements. The optimal
learning algorithm is derived for arbitrary von Neumann measurements in the
case of training with one or two examples. The analysis of the case of three
examples reveals that, differently from the learning of unitary gates, the
optimal algorithm for learning of quantum measurements cannot be parallelized,
and requires quantum memories for the storage of information.
