With wide applications ranging from quantum communication and metrology to biomedicine, single photon sources in solid-state hosts have become a major area of study. Here, we focus on three applications: nanothermometry, optically detected magnetic resonance (ODMR), and second order autocorrelation. We present novel statistical and machine learning (ML) approaches to extract information from experimental and simulated data and benchmark these methods against traditional inference-based statistical approaches. We found that compared to traditional inference-based methods ML algorithms can: i) predict temperatures at the nanoscale with greater accuracy and with less calibration points than traditional fitting methods; ii) identify the resonance peaks in ODMR spectra with factors ~1.3x and ~4.7x better accuracy and resolution and achieved equal or better performance with ~5x less data; and iii) have the potential to parse second order autocorrelation data more efficiently. ML algorithms are thus powerful tools for quantum sensing techniques.
Author Keywords: colour centers, machine learning, nanosensing, nanothermometry, optically detected magnetic resonance, second order autocorrelation