Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality

Area of Science:

• Quantum sensing
• Materials science
• Spectroscopy

Background:

• Optimizing measurement parameters is critical for experimental success.
• Nonlinear relationships between parameters and signal quality often necessitate complex trial-and-error optimization.
• Existing methods can be inefficient and may get stuck in local optima.

Purpose of the Study:

• To develop a novel machine learning-based approach for optimizing multiple, nonlinearly influencing measurement parameters.
• To enhance signal quality and reduce optimization time in experimental measurements.
• To demonstrate the efficacy of this method using optically detected magnetic resonance (ODMR) of nitrogen-vacancy (NV) centers.

Main Methods:

• Utilized machine learning, specifically linear regression, neural networks, and random forests, for parameter optimization.
• Constructed a dataset of ODMR spectra to train models for predicting optimal laser and microwave powers.
• Focused on maximizing contrast and signal-to-noise ratio (SNR) as key performance indicators.

Main Results:

• A neural network model achieved a significantly higher coefficient of determination compared to linear regression and random forests.
• The machine learning approach successfully predicted optimal laser and microwave powers for improved ODMR spectra.
• Demonstrated rapid and efficient optimization of parameters with nonlinear influence on signal quality.

Conclusions:

• The proposed machine learning method offers a novel and efficient way to set measurement parameters influenced by nonlinear relationships.
• This approach can accelerate experimental optimization in fields utilizing ODMR, such as quantum sensing and materials science.
• Potential applications extend to other spectroscopic techniques like nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), and fluorescence microscopy.