论文标题
使用QNN使用更少的培训数据来预测更好
Predict better with less training data using a QNN
论文作者
论文摘要
在过去的十年中，机器学习彻底改变了基于视力的质量评估，该质量评估现在已成为标准的卷积神经网络（CNN）。在本文中，我们考虑了该开发中的潜在下一步，并描述了有效地将经典图像数据映射到量子状态并允许可靠图像分析的Quanvolutional神经网络（QNN）算法。我们实际上演示了如何在计算机视觉中利用量子设备以及如何将量子卷积引入古典CNN。在处理工业质量控制中的现实世界用例时，我们在Pennylane框架内实施了混合QNN模型，并从经验上观察它，可以使用比经典CNN更少的培训数据实现更好的预测。换句话说，我们从经验上观察到一个真正的量子优势，对于由于卓越的数据编码而引起的工业应用。
Over the past decade, machine learning revolutionized vision-based quality assessment for which convolutional neural networks (CNNs) have now become the standard. In this paper, we consider a potential next step in this development and describe a quanvolutional neural network (QNN) algorithm that efficiently maps classical image data to quantum states and allows for reliable image analysis. We practically demonstrate how to leverage quantum devices in computer vision and how to introduce quantum convolutions into classical CNNs. Dealing with a real world use case in industrial quality control, we implement our hybrid QNN model within the PennyLane framework and empirically observe it to achieve better predictions using much fewer training data than classical CNNs. In other words, we empirically observe a genuine quantum advantage for an industrial application where the advantage is due to superior data encoding.
