Reinforcement Learning for Quantum Feedback in the Jaynes-Cummings Model and beyond

The Jaynes-Cummings model of a qubit coupled to a cavity represents one of the paradigmatic models of light-matter interaction, with experimental realizations in cavity and circuit quantum electrodynamics and phononic systems. The nonlinearity provided by the qubit can be exploited to prepare arbitrary quantum states of the cavity, and an explicit construction for the required control sequences has been provided by Law and Eberly. However, in the presence of noise and decoherence, this approach is not sufficient, and feedback strategies need to be invented. In this talk, we will show how suitably engineered techniques of reinforcement learning can efficiently discover such strategies. The same techniques can also be applied to a wide range of other settings.