3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
This work addresses the problem of feature extraction limitations in quantum neural networks for point cloud data classification, representing an incremental advancement in quantum machine learning.
The authors tackled the challenge of scaling quantum convolutional neural networks for point cloud classification by proposing a 3D scalable QCNN with reverse fidelity training, achieving desired performance as verified through data-intensive evaluation.
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.