International Conference on Information Science, Parallel and Distributed Systems (ISPDS)

Quantum Convolutional Neural Network based on Variational Quantum Fuzzy Inference

Fangcong Zheng, Z. Liu, Shibin Zhang

Published May 31, 2024• 1 citation

Abstract

In recent years, with the rapid development of quantum technology, quantum machine learning has attracted wide attention. In this paper, we propose a novel quantum machine learning model which introduces variational quantum gates in the quantum fuzzy inference circuits, and adjusts the variational gate parameters to automatically learn fuzzy rules for data classification through quantum machine learning training. Comparison results on the iris dataset show that our variational quantum fuzzy inference quantum convolutional neural network (VQFI-QCNN) outperforms classical multi layer perceptron (MLP) neural networks with a similar number of parameters, demonstrating the potential advantages of quantum. Due to the better results on the iris structured dataset, our VQFI-QCNN deserves further investigation, and as the number of quantum bits available for quantum computers increases, our method may also be applied in the future to efficiently classify structured data in mega data.

DOI: 10.1109/ispds62779.2024.10667586

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