Reservoir Computing Using Measurement-Controlled Quantum Dynamics
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911787818942464 |
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| author | Abbas, A. H. Maksymov, Ivan S. |
| author_facet | Abbas, A. H. Maksymov, Ivan S. |
| contents | Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reservoir Computing Using Measurement-Controlled Quantum Dynamics Abbas, A. H. Maksymov, Ivan S. Neural and Evolutionary Computing Artificial Intelligence Quantum Physics Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources. |
| title | Reservoir Computing Using Measurement-Controlled Quantum Dynamics |
| topic | Neural and Evolutionary Computing Artificial Intelligence Quantum Physics |
| url | https://arxiv.org/abs/2403.01024 |