Reinforcement Learning-based control of a cavity system with non linear measurement

Quantum states of the radiation field inside cavities have emerged as a promising new platform for quantum information processing. One major challenge is to find efficient ways to prepare nonclassical quantum states. Recently, deep reinforcement learning (DRL) has been introduced into quantum physics applications. We show that indeed DRL techniques can discover such strategies from scratch, by employing feedback based on nonlinear measurements, and create nonclassical states even in the absence of nonlinear controls.
We demonstrate the excellent performance of this scheme, discuss remaining challenges and potential experimental implementations.