  Gaussian boson sampling (GBS), a computational problem conjectured to be hard
to simulate on a classical machine, has been at the forefront of recent years'
experimental and theoretical efforts to demonstrate quantum advantage. The
classical intractability of the sampling task makes validating these
experiments a challenging and essential undertaking. In this paper, we propose
binned-detector probability distributions as a suitable quantity to
statistically validate GBS experiments employing photon-number-resolving
detectors. We show how to compute such distributions by leveraging their
connection with their respective characteristic function. The latter may be
efficiently and analytically computed for squeezed input states as well as for
relevant classical hypothesis like squashed states. Our scheme encompasses
other validation methods based on marginal distributions and correlation
functions. Additionally, it can accommodate various sources of noise, such as
losses and partial distinguishability, a feature that have received limited
attention within the GBS framework so far. We also illustrate how
binned-detector probability distributions behave when Haar-averaged over all
possible interferometric networks, extending known results for Fock boson
sampling.
