论文标题
使用深层和量子神经网络的交通标志分类
Traffic Sign Classification Using Deep and Quantum Neural Networks
论文作者
论文摘要
量子神经网络（QNN）是一种新兴技术，可以在包括计算机视觉在内的许多应用中使用。在本文中，我们提出了一个使用混合量子量子卷积神经网络实施的交通标志分类系统。在德国交通标志基准数据集上进行的实验表明，目前QNN的表现不佳（深度循环神经网络），但仍然提供超过90％的精度，并且绝对是高级计算机视觉的有希望的解决方案。
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.
