  Disorder in condensed matter and atomic physics is responsible for a great
variety of fascinating quantum phenomena, which are still challenging for
understanding, not to mention the relevant dynamical control. Here we introduce
proof of the concept and analyze neural network-based machine learning
algorithm for achieving feasible high-fidelity quantum control of a particle in
random environment. To explicitly demonstrate its capabilities, we show that
convolutional neural networks are able to solve this problem as they can
recognize the disorder and, by supervised learning, further produce the policy
for the efficient low-energy cost control of a quantum particle in a
time-dependent random potential. We have shown that the accuracy of the
proposed algorithm is enhanced by a higher-dimensional mapping of the disorder
pattern and using two neural networks, each properly trained for the given
task. The designed method, being computationally more efficient than the
gradient-descent optimization, can be applicable to identify and control
various noisy quantum systems on a heuristic basis.
