Tuning Nitrogen-Vacancy Centers in Diamond using Reinforcement Learning

The NV center in diamond, due to its versatility, has emerged as a leading contender for nodes in quantum networks, offering the advantage of optical control even at room temperature. However, the challenge lies in the spectral jumps in the emission frequencies, which, over time, cause the optical lasers to lose resonance. For a reliable quantum Internet, swift and efficient tuning is crucial. In this work, we developed a physical model based on the stochastic master equation, which effectively captures the essential dynamics of the NV center required for tuning. We designed a correction scheme rooted in this physical model and deep reinforcement learning. Our results demonstrate that a deep-reinforcement learning agent can successfully tune a six-level model using two tuning parameters: the voltage of the surface electrodes and the frequency of the repump laser.