  We generalize a standard benchmark of reinforcement learning, the classical
cartpole balancing problem, to the quantum regime by stabilizing a particle in
an unstable potential through measurement and feedback. We use state-of-the-art
deep reinforcement learning to stabilize a quantum cartpole and find that our
deep learning approach performs comparably to or better than other strategies
in standard control theory. Our approach also applies to measurement-feedback
cooling of quantum oscillators, showing the applicability of deep learning to
general continuous-space quantum control.
