Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits
Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum devices, parametrized quantum circuits have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally c...
| 主要な著者: | , , , , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
American Physical Society
2020
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