NV center based nano-NMR enhanced by deep learning

TL;DR

This paper demonstrates that deep learning algorithms significantly improve nano-NMR spectral discrimination and resolution by effectively learning complex noise models, outperforming traditional Bayesian methods on experimental data.

Contribution

The work introduces deep learning techniques that surpass Bayesian approaches in nano-NMR spectral analysis, especially under unknown and complex noise conditions.

Findings

DL algorithms achieve optimal frequency discrimination without prior noise knowledge.

DL outperforms Bayesian methods in noisy experimental data.

DL methods are more computationally efficient and scalable.

Abstract

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.