论文标题
一种用于从胸部X光片检测肺炎的经典量子卷积神经网络
A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs
论文作者
论文摘要
尽管已经提出了许多用于机器学习的量子计算技术，但它们在实际数据集上的性能仍有待研究。在本文中，我们探讨了如何将变异量子电路集成到经典的神经网络中，以解决从胸部X光片检测肺炎的问题。我们用一个带有变异量子电路的经典卷积神经网络的一层来创建混合神经网络。我们在包含胸部X光片的图像数据集上训练两个网络，并根据其性能进行基准测试。为了减轻不同随机性在网络训练中的影响，我们在多个回合中对结果进行了采样。我们表明，混合网络在不同的绩效指标上的表现优于经典网络，并且这些改进在统计学上很重要。我们的工作是量子计算的潜力，可以显着改善与社会和行业相关的非平凡问题的神经网络性能的潜力。
While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integrated into a classical neural network for the problem of detecting pneumonia from chest radiographs. We substitute one layer of a classical convolutional neural network with a variational quantum circuit to create a hybrid neural network. We train both networks on an image dataset containing chest radiographs and benchmark their performance. To mitigate the influence of different sources of randomness in network training, we sample the results over multiple rounds. We show that the hybrid network outperforms the classical network on different performance measures, and that these improvements are statistically significant. Our work serves as an experimental demonstration of the potential of quantum computing to significantly improve neural network performance for real-world, non-trivial problems relevant to society and industry.
