  Quantum effects are known to provide an advantage in particle transfer across
networks. In order to achieve this advantage, requirements on both a graph type
and a quantum system coherence must be found. Here we show that the process of
finding these requirements can be automated by learning from simulated
examples. The automation is done by using a convolutional neural network of a
particular type that learns to understand with which network and under which
coherence requirements quantum advantage is possible. Our machine learning
approach is applied to study noisy quantum walks on cycle graphs of different
sizes. We found that it is possible to predict the existence of quantum
advantage for the entire decoherence parameter range, even for graphs outside
of the training set. Our results are of importance for demonstration of
advantage in quantum experiments and pave the way towards automating scientific
research and discoveries.
