Generalization Study of Quantum Neural Network
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2020
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| _version_ | 1866910461163732992 |
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| author | Jiang, JinZhe Zhang, Xin Li, Chen Zhao, YaQian Li, RenGang |
| author_facet | Jiang, JinZhe Zhang, Xin Li, Chen Zhao, YaQian Li, RenGang |
| contents | Generalization is an important feature of neural network, and there have been many studies on it. Recently, with the development of quantum compu-ting, it brings new opportunities. In this paper, we studied a class of quantum neural network constructed by quantum gate. In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state. Since all the operations in quan-tum neural networks are unitary, the parameters constitute a hypersphere of Hilbert space. Compared with traditional neural network, the parameter space is flatter. Therefore, it is not easy to fall into local optimum, which means the quantum neural networks have better generalization. In order to validate our proposal, we evaluated our model on three public datasets, the results demonstrated that our model has better generalization than the classical neu-ral network with the same structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_02388 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Generalization Study of Quantum Neural Network Jiang, JinZhe Zhang, Xin Li, Chen Zhao, YaQian Li, RenGang Quantum Physics Machine Learning I.2.6 Generalization is an important feature of neural network, and there have been many studies on it. Recently, with the development of quantum compu-ting, it brings new opportunities. In this paper, we studied a class of quantum neural network constructed by quantum gate. In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state. Since all the operations in quan-tum neural networks are unitary, the parameters constitute a hypersphere of Hilbert space. Compared with traditional neural network, the parameter space is flatter. Therefore, it is not easy to fall into local optimum, which means the quantum neural networks have better generalization. In order to validate our proposal, we evaluated our model on three public datasets, the results demonstrated that our model has better generalization than the classical neu-ral network with the same structure. |
| title | Generalization Study of Quantum Neural Network |
| topic | Quantum Physics Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2006.02388 |