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arXiv · 2209.12372

Scalable Quantum Convolutional Neural Networks

Hankyul Baek·Won Joon Yun·Joongheon Kim
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Abstract

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next generation of QNN because it can process high-dimensional vector input. However, due to the nature of quantum computing, it is difficult for the classical QCNN to extract a sufficient number of features. Motivated by this, we propose a new version of QCNN, named scalable quantum convolutional neural network (sQCNN). In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN.

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Hankyul Baek, Won Joon Yun, Joongheon Kim. 2022-09-26. Scalable Quantum Convolutional Neural Networks. https://arxiv.org/abs/2209.12372

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