Abstract

We propose and test a Magnetic Field Learning (MFL) protocol for high-resolution, high dynamic range and high-sensitivity magnetometry with a single NV-center electron spin. Our approach leverages recent proposals that analyze the benefits of adopting classical machine learning to post-process quantum data in quantum sensing protocols [1]. MFL was tested at room–temperature using a setup detecting state–dependent fluorescence via confocal microscopy, and a microwave controlled NV defect in bulk diamond as a sensor [2]. This setup senses the intensity of a magnetic field B in the proximity of the quantum sensor via Ramsey interferometry [3]. In our protocol, the Ramsey precession time τi is adaptively chosen at each step via an efficient particle guess heuristic [1].

© 2019 IEEE

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