Multi-Class Quantum Convolutional Neural Networks
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929320683896832 |
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| author | Mordacci, Marco Ferrari, Davide Amoretti, Michele |
| author_facet | Mordacci, Marco Ferrari, Davide Amoretti, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Class Quantum Convolutional Neural Networks Mordacci, Marco Ferrari, Davide Amoretti, Michele Quantum Physics Emerging Technologies Machine Learning 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. |
| title | Multi-Class Quantum Convolutional Neural Networks |
| topic | Quantum Physics Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2404.12741 |