3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
Abstract
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.
Cite
@article{arxiv.2210.09728,
title = {3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications},
author = {Hankyul Baek and Won Joon Yun and Joongheon Kim},
journal= {arXiv preprint arXiv:2210.09728},
year = {2022}
}