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A device called a 'Gaussian Boson Sampler' has initially been proposed as anear-term demonstration of classically intractable quantum computation. Asrecently shown, it can also be used to decide whether two graphs areisomorphic. Based on these results we construct a feature map and graphsimilarity measure or 'graph kernel' using samples from the device. We showthat the kernel performs well compared to standard graph kernels on typicalbenchmark datasets, and provide a theoretical motivation for this success,linking the distribution of a Gaussian Boson Sampler to the number of matchingsin subgraphs. Our results contribute to a new way of thinking about kernels asa (quantum) hardware-efficient feature mapping, and lead to an interestingapplication for near-term quantum computing.

A quantum hardware-induced graph kernel based on Gaussian Boson Sampling