Stereoscopic scalable quantum convolutional neural networks

Abstract

As the noisy intermediate-scale quantum (NISQ) era has begun, a quantum neural network (QNN) is definitely a promising solution to many problems that classical neural networks cannot solve. In addition, a quantum convolutional neural network (QCNN) is now receiving a lot of attention because it can process high dimensional inputs comparing 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. This is especially challenging in classification operations with high-dimensional data input. 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. This is especially challenging in classification operations with high dimensional data input. Motivated by this, a novel stereoscopic 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.

Keywords: Point cloud classification; Quantum convolutional neural network; Quantum deep learning.