  We present a variation of a quantum algorithm for the machine learning task
of classification with graph-structured data. The algorithm implements a
feature extraction strategy that is based on Gaussian boson sampling (GBS) a
near term model of quantum computing. However, unlike the currently proposed
algorithms for this problem, our GBS setup only requires binary (light/no
light) detectors, as opposed to photon number resolving detectors. These
detectors are technologically simpler and can operate at room temperature,
making our algorithm less complex and less costly to implement on the physical
hardware. We also investigate the connection between graph theory and the
matrix function called the Torontonian which characterizes the probabilities of
binary GBS detection events.
