  The growing field of nano nuclear magnetic resonance (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 detrimental inherent noise. This strong noise masks to the weak
signal and results in a very low signal-to-noise ratio. Moreover, the noise
model is usually complex and unknown, which renders the data processing of the
measurement results very complicated. Hence, spectra discrimination is hard to
achieve and in particular, it is difficult to reach the optimal discrimination.
In this work we present strong indications that this difficulty can be overcome
by deep learning (DL) algorithms. The DL algorithms can mitigate the
adversarial effects of the noise efficiently by effectively learning the noise
model. We show that in the case of frequency discrimination DL algorithms reach
the optimal discrimination without having any pre-knowledge of the physical
model. Moreover, the DL discrimination scheme outperform Bayesian methods when
verified on noisy experimental data obtained by a single Nitrogen-Vacancy (NV)
center. In the case of frequency resolution 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 DL algorithms also emerge as
much more efficient in terms of computational resources and run times. Since in
many real-world scenarios the noise is complex and difficult to model, we argue
that DL is likely to become a dominant tool in the field.
