  We present a classical control mechanism for Quantum devices using
Reinforcement Learning. Our strategy is applied to the Quantum Approximate
Optimization Algorithm (QAOA) in order to optimize an objective function that
encodes a solution to a hard combinatorial problem. This method provides
optimal control of the Quantum device following a reformulation of QAOA as an
environment where an autonomous classical agent interacts and performs actions
to achieve higher rewards. This formulation allows a hybrid classical-Quantum
device to train itself from previous executions using a continuous formulation
of deep Q-learning to control the continuous degrees of freedom of QAOA. Our
approach makes a selective use of Quantum measurements to complete the
observations of the Quantum state available to the agent. We run tests of this
approach on MAXCUT instances of size up to N = 21 obtaining optimal results. We
show how this formulation can be used to transfer the knowledge from shorter
training episodes to reach a regime of longer executions where QAOA delivers
higher results.

Quantum Physics;Machine Learning