NV center base nano-NMR enhanced by deep learning

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The growing field of nano-NMR seeks to estimate spectra or discriminate between spectra of minuscule amounts of complex molecules. While this field holds great promise, nano-NMR experiments suffer from adverse inherent noise. In this work we present strong indications that deep learning algorithms can efficiently mitigate the adversarial effects of noise. Over a wide range of scenarios we show that this approach outperforms Bayesian methods even when the latter have full pre-knowledge of the noise model and the former has none. These the deep learning algorithms also emerge as much more efficient in terms of computational resources and run times. On the basis of various real-world scenarios in which the noise is complex and difficult to model, we argue that deep learning is likely to become a dominant tool in the field.