Multi-Class Quantum Convolutional Neural Networks

Marco Mordacci, Davide Ferrari, Michele Amoretti
4/19/2024
quant-phcs.ETcs.LG

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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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Cite this paper

@article{mordacci2024multiclass,
  title  = {Multi-Class Quantum Convolutional Neural Networks},
  author = {Marco Mordacci and Davide Ferrari and Michele Amoretti},
  year   = {2024},
  eprint = {2404.12741},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.12741v1}
}

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