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.
翻译:量子神经网络(QNN)是一种新兴技术,可用于包括计算机视觉在内的许多应用。在本文中,我们提出了一个使用混合量子古典神经神经网络实施的交通标志分类系统。 在德国交通信号识别基准数据集上的实验显示,目前QNN并不比经典的DCNN(深共振神经网络)高,但仍然提供90%的准确性,而且对于先进的计算机视觉来说绝对是很有希望的解决方案。