  In this paper, we introduce a quantum extension of classical DNN, QDNN. The
QDNN consisting of quantum structured layers can uniformly approximate any
continuous function and has more representation power than the classical DNN.
It still keeps the advantages of the classical DNN such as the non-linear
activation, the multi-layer structure, and the efficient backpropagation
training algorithm. Moreover, the QDNN can be used on near-term noisy
intermediate-scale quantum processors. A numerical experiment for image
classification based on quantum DNN is given, where a high accuracy rate is
achieved.
