NV center based nano-NMR enhanced by deep learning.

Area of Science:

• Spectroscopy
• Quantum Sensing
• Machine Learning

Background:

• Nano nuclear magnetic resonance (nano-NMR) is crucial for analyzing minute molecular samples.
• Nano-NMR experiments are severely limited by inherent noise, leading to low signal-to-noise ratios.
• Complex and unknown noise models complicate data processing and hinder optimal spectra discrimination.

Purpose of the Study:

• To investigate the efficacy of deep learning (DL) algorithms in overcoming noise limitations in nano-NMR.
• To demonstrate DL's ability to learn noise models and improve spectra discrimination and resolution.
• To compare DL performance against established methods like Bayesian approaches.

Main Methods:

• Application of deep learning algorithms to nano-NMR data processing.
• Evaluation of DL algorithms for spectra discrimination and frequency resolution tasks.
• Comparison of DL methods with Bayesian approaches using experimental data from a single Nitrogen-Vacancy (NV) center.

Main Results:

• DL algorithms effectively mitigate noise by learning complex noise models without prior physical knowledge.
• DL achieves optimal spectra discrimination, surpassing Bayesian methods on noisy experimental data.
• DL demonstrates superior frequency resolution compared to Bayesian methods, even when noise models are known to the latter.

Conclusions:

• Deep learning offers a powerful solution to the inherent noise challenges in nano-NMR.
• DL algorithms provide superior performance in spectra discrimination and resolution, outperforming traditional methods.
• DL's efficiency and ability to handle complex noise suggest its future dominance in nano-NMR data analysis.