  We explore the use of machine-learning techniques to detect quantum speedup
in random walks on graphs. Specifically, we investigate the performance of
three different neural-network architectures (variations on fully connected and
convolutional neural networks) for identifying linear, cyclic, and random
graphs that yield quantum speedups in terms of the hitting time for reaching a
target node after starting in another node of the graph. Our results indicate
that carefully building the data set for training can improve the performance
of the neural networks, but all architectures we test struggle to classify
large random graphs and generalize from training on one graph size to testing
on another. If classification accuracy can be improved further, valuable
insights about quantum advantage may be gleaned from these neural networks, not
only for random walks, but more generally for quantum computing and quantum
transport.
