  One of the main characteristics of optical imaging systems is the spatial
resolution, which is restricted by the diffraction limit to approximately half
the wavelength of the incident light. Along with the recently developed
classical super-resolution techniques, which aim at breaking the diffraction
limit in classical systems, there is a class of quantum super-resolution
techniques which leverage the non-classical nature of the optical signals
radiated by quantum emitters, the so-called antibunching super-resolution
microscopy. This approach can ensure a factor of $\sqrt{n}$ improvement in the
spatial resolution by measuring the n-th order autocorrelation function. The
main bottleneck of the antibunching super-resolution microscopy is the
time-consuming acquisition of multi-photon event histograms. We present a
machine learning-assisted approach for the realization of rapid antibunching
super-resolution imaging and demonstrate 12 times speed-up compared to
conventional, fitting-based autocorrelation measurements. The developed
framework paves the way to the practical realization of scalable quantum
super-resolution imaging devices that can be compatible with various types of
quantum emitters.
