  A device called a 'Gaussian Boson Sampler' has initially been proposed as a
near-term demonstration of classically intractable quantum computation. As
recently shown, it can also be used to decide whether two graphs are
isomorphic. Based on these results we construct a feature map and graph
similarity measure or 'graph kernel' using samples from the device. We show
that the kernel performs well compared to standard graph kernels on typical
benchmark datasets, and provide a theoretical motivation for this success,
linking the distribution of a Gaussian Boson Sampler to the number of matchings
in subgraphs. Our results contribute to a new way of thinking about kernels as
a (quantum) hardware-efficient feature mapping, and lead to an interesting
application for near-term quantum computing.
