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The paper applies machine learning to quantum sensing: a model learns to localise a magnetic target from raw NV-diamond magnetometer readings, without an explicit physical model of the field. The reported results are about 80% accuracy at 10 Hz within a 30 cm error tolerance, but they come from a single axis of motion in one experimental setup. Readers should watch for whether the approach holds up in multi-dimensional, less controlled environments.
Machine learning now tracks magnetic objects with a diamond quantum sensor
Machine learning now predicts magnetic object positions with 80% accuracy, using data from a quantum magnetometer based on nitrogen-vacancy centers in diamond.
Researchers at the University of Melbourne and RMIT University have developed a machine learning method that predicts the position of a magnetic target in real-time, trained solely on experimental data and bypassing the need for pre-existing physical models. The work uses a quantum diamond magnetometer to track an elevator’s movements, achieving over 80% accuracy at a rate of 10 Hz with a positional error tolerance of 30 centimeters, a precise measurement compared to the 4-meter spacing between parking levels. This approach offers a new path for applications including ground transport monitoring, underground…
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