Quantum magnetometry enhanced by machine learning

Abstract Quantum optimal control in color center physics plays a crucial role in advancing sensor technology. This study focuses on optimizing microwave pulse shapes within a Ramsey sequence for nitrogen-vacancy centers to enhance sensor sensitivity and signal detection capabilities. We compare state-of-the-art optimization methods, including the dressed chopped randomized basis Nelder–Mead algorithm and covariance matrix adaptation evolutionary strategy, and extend our search to machine learning approaches, such as Gaussian processes and artificial neural networks. These machine learning techniques are specifically designed to provide robust and global solutions that can rapidly adapt to changing environmental conditions. Our results demonstrate more than a sixfold increase in convergence speed compared to conventional methods and considerable contrast improvements with a limited retraining set of 72 samples. Furthermore, we demonstrate that the optimized Ramsey contrast translates into a significant enhancement in the signal-to-noise ratio for detecting synthetic magnetic heart signals. This highlights the potential of machine learning-driven quantum optimal control for developing more flexible, adaptive, and efficient quantum sensing solutions in real-world scenarios.