  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.
