  Deep learning has been shown to be able to recognize data patterns better
than humans in specific circumstances or contexts. In parallel, quantum
computing has demonstrated to be able to output complex wave functions with a
few number of gate operations, which could generate distributions that are hard
for a classical computer to produce. Here we propose a hybrid quantum-classical
convolutional neural network (QCCNN), inspired by convolutional neural networks
(CNNs) but adapted to quantum computing to enhance the feature mapping process.
QCCNN is friendly to currently noisy intermediate-scale quantum computers, in
terms of both number of qubits as well as circuit's depths, while retaining
important features of classical CNN, such as nonlinearity and scalability. We
also present a framework to automatically compute the gradients of hybrid
quantum-classical loss functions which could be directly applied to other
hybrid quantum-classical algorithms. We demonstrate the potential of this
architecture by applying it to a Tetris dataset, and show that QCCNN can
accomplish classification tasks with learning accuracy surpassing that of
classical CNN.
