  Topologically ordered materials may serve as a platform for new quantum
technologies such as fault-tolerant quantum computers. To fulfil this promise,
efficient and general methods are needed to discover and classify new
topological phases of matter. We demonstrate that deep neural networks
augmented with external memory can use the density profiles formed in quantum
walks to efficiently identify properties of a topological phase as well as
phase transitions. On a trial topological ordered model, our method's accuracy
of topological phase identification reaches 97.4%, and is shown to be robust to
noise on the data. Furthermore, we demonstrate that our trained DNN is able to
identify topological phases of a perturbed model and predict the corresponding
shift of topological phase transitions without learning any information about
the perturbations in advance. These results demonstrate that our approach is
generally applicable and may be used to identify a variety of quantum
topological materials.
