Multi-Class Quantum Convolutional Neural Networks

• URL: http://arxiv.org/abs/2404.12741v1
• Date: Fri, 19 Apr 2024 09:36:48 GMT
• Title: Multi-Class Quantum Convolutional Neural Networks
• Authors: Marco Mordacci, Davide Ferrari, Michele Amoretti,
• Abstract summary: We propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes.
• Score: 2.6422127672474933
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.

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