Layered Uploading for Quantum Convolutional Neural Networks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918017847263232 |
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| author | Barrué, Grégoire Quertier, Tony Zang, Orlane |
| author_facet | Barrué, Grégoire Quertier, Tony Zang, Orlane |
| contents | Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along the quantum circuit. This allows us to use more features from the data, hence giving to the algorithm more information, without having to increase the number of qubits that we use for the quantum circuit. This approach is motivated by the fact that we do not always have great amounts of data, and that quantum computers are currently restricted in their number of logical qubits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Layered Uploading for Quantum Convolutional Neural Networks Barrué, Grégoire Quertier, Tony Zang, Orlane Quantum Physics Cryptography and Security Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along the quantum circuit. This allows us to use more features from the data, hence giving to the algorithm more information, without having to increase the number of qubits that we use for the quantum circuit. This approach is motivated by the fact that we do not always have great amounts of data, and that quantum computers are currently restricted in their number of logical qubits. |
| title | Layered Uploading for Quantum Convolutional Neural Networks |
| topic | Quantum Physics Cryptography and Security |
| url | https://arxiv.org/abs/2404.09750 |