  Artificial neural networks have achieved great success in many fields ranging
from image recognition to video understanding. However, its high requirements
for computing and memory resources have limited further development on
processing big data with high dimensions. In recent years, advances in quantum
computing show that building neural networks on quantum processors is a
potential solution to this problem. In this paper, we propose a novel neural
network model named Quantum Convolutional Neural Network (QCNN), aiming at
utilizing the computing power of quantum systems to accelerate classical
machine learning tasks. The designed QCNN is based on implementable quantum
circuits and has a similar structure as classical convolutional neural
networks. Numerical simulation results on the MNIST dataset demonstrate the
effectiveness of our model.
