Multi-Class Quantum Convolutional Neural Networks
<|reference_start|>Multi-Class Quantum Convolutional Neural Networks: 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.<|reference_end|>
@article{mordacci2024multi-class,
    title={Multi-Class Quantum Convolutional Neural Networks},
    author={Marco Mordacci, Davide Ferrari, Michele Amoretti},
    journal={arXiv preprint arXiv:2404.12741},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12741},
    primaryClass={quant-ph cs.ET cs.LG}
}