Accurate magnetic field imaging using nanodiamond quantum sensors enhanced by machine learning

Area of Science:

• Quantum physics
• Materials science
• Machine learning

Background:

• Nanodiamonds are promising quantum sensors for precise local magnetic field measurements.
• Existing methods for magnetic field imaging have limitations in accuracy and applicability.

Purpose of the Study:

• To demonstrate magnetic field imaging with high accuracy using nanodiamond ensembles and machine learning.
• To explore the potential of nanodiamond ensembles for vector magnetometry.

Main Methods:

• Utilized a nanodiamond ensemble (NDE) for sensing magnetic fields.
• Applied machine learning algorithms without relying on physical models for data analysis and imaging.
• Investigated the NDE signal's dependence on magnetic field direction.

Main Results:

• Achieved high-accuracy magnetic field imaging with 1.8 µT precision.
• Discovered a significant dependence of the NDE signal on magnetic field direction.
• Demonstrated the capability to visualize nano-magnetism and mesoscopic currents.

Conclusions:

• The combination of NDE and machine learning offers a powerful tool for advanced magnetic field sensing.
• The findings support the use of NDE for vector magnetometry and suggest improvements to existing models.
• This approach expands the applicability of nanodiamond quantum sensing to complex materials and biological systems.