  A crucial step in the race towards quantum advantage is optimizing quantum
annealing using ad-hoc annealing schedules. Motivated by recent progress in the
field, we propose to employ long-short term memory (LSTM) neural networks to
automate the search for optimal annealing schedules for random Ising models on
regular graphs. By training our network using locally-adiabatic annealing
paths, we are able to predict optimal annealing schedules for unseen instances
and even larger graphs than those used for training.
