Figure | The structure of the correlated optical convolutional neural network and its performance. (IMAGE)
Caption
a, an illustrative scenario for correlated optical convolutional neural network. The basic structure involves four parts: the correlated light source (colored by brown), the convolution (colored by blue), the pooling (colored by orange), and the detections (colored by black). The strategies for implementing the convolution and the pooling are instructed in the dashed boxes. The basic elements include waveplates, interferometers, and nonlinear optical devices. b, the training performance of correlated optical convolutional neural network (green line) in a binary classification task, compared with classical convolutional neural network (blue line). The curves show that correlated optical convolutional neural network converges faster than the classical one. c, the training performance of correlated optical convolutional neural network (magenta line) in a four-classification task, compared with classical convolutional neural network (red line). The curves also show that correlated optical convolutional neural network converges faster than the classical one. d, the experimental output of the correlated optical convolutional neural network for identifying the topological phase of quantum states. The left panel show the experimental results, whose x-y coordinates are the parameters of the states. By taking the second order derivative of the outputs, the phase boundaries can be obtained. The right panel gives the comparison of the boundaries obtained by experiments with those calculated by standard methods. The two results match well, further validating the correspondence between correlated optical convolutional neural networks and quantum convolutional networks.
Credit
by Yifan Sun, Qian Li, Ling-Jun Kong, and Xiangdong Zhang
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License
CC BY