A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929548617056256 |
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| author | Wang, Yunfei Liu, Junyu |
| author_facet | Wang, Yunfei Liu, Junyu |
| contents | Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance Wang, Yunfei Liu, Junyu Quantum Physics Artificial Intelligence Machine Learning Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning. |
| title | A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.11351 |