  We propose a reinforcement learning (RL) scheme for feedback quantum control
within the quan-tum approximate optimization algorithm (QAOA). QAOA requires a
variational minimization for states constructed by applying a sequence of
unitary operators, depending on parameters living ina highly dimensional space.
We reformulate such a minimum search as a learning task, where a RL agent
chooses the control parameters for the unitaries, given partial information on
the system. We show that our RL scheme finds a policy converging to the optimal
adiabatic solution for QAOA found by Mbeng et al. arXiv:1906.08948 for the
translationally invariant quantum Ising chain. In presence of disorder, we show
that our RL scheme allows the training part to be performed on small samples,
and transferred successfully on larger systems.
