Quantum sensors operated in real time: from Bayesian inference to machine learning

Quantum effects have been shown to enhance sensitivity in probing external perturbations across diverse platforms, from solid-state devices to cold atomic gases. While this capability has driven important breakthroughs in quantum metrology, the vast majority of real-life applications require quantum sensors to track time-varying signals—such as gravitational waves generated by black holes merging, or fluctuating magnetic fields generated by the human brain. In my talk, I will summarise recent results obtained within my group, in which we combine the description of continuously monitored quantum sensors with methods of signal processing, so that quantum effects can still be used to boost the sensitivity of such devices in "real time".