Quantum memristors for neuromorphic quantum machine learning
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913627282341888 |
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| author | Lamata, Lucas |
| author_facet | Lamata, Lucas |
| contents | Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. Thus, an efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18979 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum memristors for neuromorphic quantum machine learning Lamata, Lucas Quantum Physics Neural and Evolutionary Computing Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. Thus, an efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled. |
| title | Quantum memristors for neuromorphic quantum machine learning |
| topic | Quantum Physics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.18979 |