论文标题
3D可伸缩量子卷积神经网络用于分类应用中的点云数据处理
3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
论文作者
论文摘要
随着嘈杂的中间尺度量子（NISQ）时代的开始，量子神经网络（QNN）最近已成为解决经典神经网络无法解决的几个特定问题的解决方案。此外，量子卷积神经网络（QCNN）是CNN的量子version，因为它可以与QNN相比处理高维矢量输入。但是，由于量子计算的性质，由于高原贫瘠而难以扩大QCNN的规模来提取足够数量的特征。由此激励，提出了一种新颖的3D可伸缩QCNN（SQCNN-3D），以用于分类应用中的点云数据处理。此外，在SQCNN-3D的顶部还考虑了反向保真训练（RF-Train），用于使用量子计算的保真度，具有有限数量的Qubits的多样化特征。我们的数据密集型性能评估验证了所提出的算法是否达到了期望的性能。
With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classical neural networks cannot solve. Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN. However, due to the nature of quantum computing, it is difficult to scale up the QCNN to extract a sufficient number of features due to barren plateaus. Motivated by this, a novel 3D scalable QCNN (sQCNN-3D) is proposed for point cloud data processing in classification applications. Furthermore, reverse fidelity training (RF-Train) is additionally considered on top of sQCNN-3D for diversifying features with a limited number of qubits using the fidelity of quantum computing. Our data-intensive performance evaluation verifies that the proposed algorithm achieves desired performance.
