  Quantum-inspired neural network is one of the interesting researches at the
junction of the two fields of quantum computing and deep learning. Several
models of quantum-inspired neurons with real parameters have been proposed,
which are mainly used for three-layer feedforward neural networks. In this
work, we improve the quantum-inspired neurons by exploiting the complex-valued
weights which have richer representational capacity and better non-linearity.
We then extend the method of implementing the quantum-inspired neurons to the
convolutional operations, and naturally draw the models of quantum-inspired
convolutional neural networks (QICNNs) capable of processing high-dimensional
data. Five specific structures of QICNNs are discussed which are different in
the way of implementing the convolutional and fully connected layers. The
performance of classification accuracy of the five QICNNs are tested on the
MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform
better in classification accuracy on MNIST dataset than the classical CNN. More
learning tasks that our QICNN can outperform the classical counterparts will be
found.
