Accelerating quantum optics experiments using statistical learning

Quantum optics experiments, involving the measurement of low-probability photon events, are known to be extremely time-consuming. In this talk, we present a new methodology for accelerating such experiments using simple statistical learning techniques such as Bayesian maximum a posteriori estimation based on few-shot data. We show it is possible to reconstruct time dependent data using a small number of detected photons, allowing for fast estimates in under a minute, and providing a one-to-two order of magnitude speed up in data acquisition time. We test our approach using real experimental data to retrieve the $G^2\tau)$ time trace for thermal light emitted by a Neon light source as well as anti-bunched light emitted by a quantum dot driven with periodic laser pulses. We also show, through numerical simulations, that our approach can be used to accelerate sub-diffraction image reconstruction based on $G^2(\tau)$ coincidence measurements. The proposed methodology has the potential to impact the scientific discovery process across a multitude of domains.