ML enhanced magnetic noise modeling for quantum magnetometry

Magnetic field sensing is intrinsically sensitive to its environment. In this project, we are developing compensation tools to mitigate against different types of magnetic noise to enhance the accuracy of a quantum magnetometer and of a platform including multiple magnetometers. Data will be collected at various magnetic characterization facilities to inform a ML based correction algorithm to cancel for hard, soft and temperature effects. The results will be directly applied to magnetic survey equipment on ground, air and eventually space to build accurate magnetic maps. Such tools will enhance geological interpretations in mining, refine the accuracy of the World Magnetic Model and ease magnetic sensing deployment across platforms, while enhancing the performance of a diamond based sensor.

Faculty Supervisor:

Foutse Khomh

Student:

Partner:

SB Quantum

Discipline:

Engineering

Sector:

Aerospace; Environmental Science and Technology; Mining; Quantum Science

University:

Polytechnique Montréal

Program: