Abstract:
Practical NV-center magnetometry requires both accurate modeling of open quantum dynamics and robust control under environmental drift. However, commonly used perturbative descriptions inadequately capture dissipative processes, while existing control strategies remain individually limited: open-loop protocols cannot adapt to unknown frequency variations, Lyapunov feedback typically converges only to a neighborhood of the optimal sensing state, and reinforcement learning alone lacks stability guarantees and requires extensive training. Here we develop a Lindblad-based framework for one effective NV center that explicitly incorporates relaxation and dephasing; the analytical model does not describe a collective many-NV ensemble. We introduce a dissipation-triggered feedback mechanism that restores high-sensitivity states following spontaneous emission events. Building on this model, we propose a hybrid control architecture that combines Lyapunov stabilization with reinforcement-learning-based adaptive correction to compensate unknown Hamiltonian drift. Robustness tests over detuning and microwave-amplitude variations show that Hybrid attains the highest QFI, substantially lower cross-realization dispersion than Pure RL and Robust GRAPE, faster fixed-budget learning than Pure RL, and substantially lower offline dynamical-propagation cost with smoother control modulation than Robust GRAPE. This physics-informed and data-driven strategy provides a robust and resource-efficient approach for maintaining high quantum Fisher information in realistic NV sensing environments.