Cat or dog? Can a Hong-Ou-Mandel interferometer tell the difference? This talk presents a quantum optical method for binary classification that recognizes patterns without the need for image reconstruction. By encoding both data and model parameters into single-photon states and leveraging two-photon interference, the system classifies patterns directly through coincidence rates. Acting as a quantum analogue of a classical neuron, it operates—once trained—with constant O(1) resource complexity, achieving a superexponential speedup over its classical counterpart.