Published paper in Quantum Science and Technology on March 2019

Classical machine learning algorithms can provide insights on high-dimensional processes that are hardly accessible with conventional approaches. In this work we apply t-distributed Stochastic Neighbor Embedding (t-SNE) to probe the spatial distribution of n-photon events in m-dimensional Hilbert spaces, showing that its findings can be beneficial for validating genuine quantum interference in boson sampling experiments. We envisage that this approach will inspire further theoretical investigations, for instance for a reliable assessment of quantum computational advantage.

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