Magnetisation Reconstruction for Quantum Metrology

Widefield nitrogen-vacancy (NV) magnetometry presents a promising method for the detection of cancer biomarkers, offering a new frontier in medical diagnostics. The challenge lies in the inverse problem of accurately reconstructing magnetisation sources from magnetic field measurements, a task complicated by the noise sensitivity of the data, and the ill-posed nature of the inverse problem. To address this, we employed a physics informed neural network (PINN) on 2D magnetic materials, combining the strengths of convolutional neural networks (CNN) with underlying physical laws of magnetism. The physics informed loss during the training of the neural network constrains the parameter space to physically plausible reconstructions. The physics-constraining results in improved accuracy and noise robustness. This paves the way for understanding the requirements for the development of such models for quantum sensing in biomedicine.