Fourier series weight in quantum machine learning
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913242335412224 |
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| author | Atchade-Adelomou, Parfait Larson, Kent |
| author_facet | Atchade-Adelomou, Parfait Larson, Kent |
| contents | In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning leveraged on the Hamiltonian encoding. With a subtle change, we performed the trigonometric interpolation, binary and multiclass classifier, and a quantum signal processing application. We also proposed a block diagram of determining approximately the Fourier coefficient based on quantum machine learning. We performed and tested all the proposed models using the Pennylane framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_00105 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fourier series weight in quantum machine learning Atchade-Adelomou, Parfait Larson, Kent Quantum Physics Machine Learning In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning leveraged on the Hamiltonian encoding. With a subtle change, we performed the trigonometric interpolation, binary and multiclass classifier, and a quantum signal processing application. We also proposed a block diagram of determining approximately the Fourier coefficient based on quantum machine learning. We performed and tested all the proposed models using the Pennylane framework. |
| title | Fourier series weight in quantum machine learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2302.00105 |