Quantum circuits based on single photons and linear optical elements (PhQC) are an ideal candidate to enable quantum information processing at room temperature. Although PhQC cannot achieve universality without post-selection, boson sampling (a well-known sampling problem) has a non-trivial computational complexity related to #P-hard. The key question now is how to utilize that complexity for practical problems? Here we present a new hybrid quantum / classical neural network model for image reorganization that achieves a 96.6% testing accuracy only 4 photons and 16 modes without PhQC optimization. We also show it outperforms the situation where coherent light sources are used.
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