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.
翻译:随着杂乱的中间级量子(NISQ)时代的开始,量子神经网络(QNN)最近成为一些古典神经网络无法解决的具体问题的解决方案。此外,量子进化神经网络(QCNN)是CNN的量子版,因为它能够处理高维矢量输入,而QNN却与QNN形成对照。然而,由于量子计算的性质,很难扩大QCNN来提取足够数量的因贫瘠高原而导致的特性。为此,为分类应用中的点云数据处理提出了一个新的3D可扩缩的QCNN(sQCNN-3D)建议。此外,在SCNN-3D的顶端还考虑反向忠诚培训(RF-Train),以利用量子计算实现数量有限的量子多样化特征。我们的数据密集性业绩评估证实,拟议的算法达到了理想的性能。