Fig. 3: Non-convex classification and experimental setup.
From: Photonic neuromorphic computing using symmetry-protected zero modes in coupled nanolaser arrays
a Definitions of the convex and non-convex regimes. b Illustration of the XNOR problem. The dots represent the input-output states of an XNOR gate, the solid line denotes the decision boundary (hyperplane) achievable by a single-layer perceptron and the dashed lines represent the decision boundaries obtained by a multilayer ANN. The positive responses (output = 1) are marked in orange, and the negative responses (output = 0) are marked in green. Clearly, the single-layer perceptron is unable to correctly represent this non-convex classification task. c Experimental setup. Two representative system responses are shown on the left: the top panel corresponds to a positive response, characterized by lasing of the quasi-zero modes, whereas the lower panel demonstrates a negative response, in which the zero mode remains off. On the right side, representative pump patterns generated using the SLM are depicted, with white circles indicating the cavity positions. The schematic of the coupled cavity array is shown at the bottom of the figure, where the boxes highlight the photonic barriers engineered to introduce asymmetric couplings (hole radii modification: −17%).
