Scalable Quantum Convolutional Neural Networks

• URL: http://arxiv.org/abs/2209.12372v1
• Date: Mon, 26 Sep 2022 02:07:00 GMT
• Title: Scalable Quantum Convolutional Neural Networks
• Authors: Hankyul Baek, Won Joon Yun, Joongheon Kim
• Abstract summary: We propose a new version of quantum neural network (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.
• Score: 12.261689483681145
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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