  Quantum optics experiments, involving the measurement of low-probability
photon events, are known to be extremely time-consuming. We present a new
methodology for accelerating such experiments using physically-motivated
ansatzes together with simple statistical learning techniques such as Bayesian
maximum a posteriori estimation based on few-shot data. We show that 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 second order intensity
correlation function, $G^{(2)}(\tau)$, as a function of time delay $\tau$
between detector counts, for thermal light as well as anti-bunched light
emitted by a quantum dot driven by periodic laser pulses. The proposed
methodology has a wide range of applicability and has the potential to impact
the scientific discovery process across a multitude of domains.
