Speaker
Description
Quantum sensing exploits quantum systems to measure physical signals with exceptional sensitivity, often producing complex and noise-corrupted datasets in which the quantities of interest must be inferred from subtle changes in quantum optical spectra. Understanding and modelling light–matter interactions through a combination of computationally-expensive physics-based simulations and data-driven techniques is essential for the development of novel sensor architectures, new applications, and the optimisation of sensor operation.
This talk will provide an overview of how data-driven modelling, machine learning, and statistical inference can enhance signal recovery, improve parameter estimation, and optimise sensor performance. Particular emphasis will be placed on methodologies that combine physical models with AI-based signal processing to extract information from noisy, high-dimensional measurements. Examples will include radio-frequency sensing with Rydberg atoms and advanced magnetometry using colour defects in diamond.