  Quantum Neural Networks (QNNs) are an emerging technology that can be used in
many applications including computer vision. In this paper, we presented a
traffic sign classification system implemented using a hybrid quantum-classical
convolutional neural network. Experiments on the German Traffic Sign
Recognition Benchmark dataset indicate that currently QNN do not outperform
classical DCNN (Deep Convolutuional Neural Networks), yet still provide an
accuracy of over 90% and are a definitely promising solution for advanced
computer vision.
