论文标题
用于高能物理数据分析的量子卷积神经网络
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
论文作者
论文摘要
这项工作提出了用于高能量物理事件分类的量子卷积神经网络（QCNN）。使用来自深层中微子实验的模拟数据集测试了所提出的模型。所提出的架构证明了比经典卷积神经网络（CNN）在类似数量的参数下更快地学习的量子优势。除了更快的收敛速度外，与CNN相比，QCNN还具有更高的测试精度。基于实验结果，研究QCNN和其他量子机学习模型在高能量物理和其他科学领域中的应用是一个有希望的方向。
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to faster convergence, the QCNN achieves greater test accuracy compared to CNNs. Based on experimental results, it is a promising direction to study the application of QCNN and other quantum machine learning models in high energy physics and additional scientific fields.
