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We present a variation of a quantum algorithm for the machine learning taskof classification with graph-structured data. The algorithm implements afeature extraction strategy that is based on Gaussian boson sampling (GBS) anear term model of quantum computing. However, unlike the currently proposedalgorithms for this problem, our GBS setup only requires binary (light/nolight) detectors, as opposed to photon number resolving detectors. Thesedetectors are technologically simpler and can operate at room temperature,making our algorithm less complex and less costly to implement on the physicalhardware. We also investigate the connection between graph theory and thematrix function called the Torontonian which characterizes the probabilities ofbinary GBS detection events.

Sample efficient graph classification using binary Gaussian boson sampling