Detecting Markovianity of Quantum Processes via Recurrent Neural Networks
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_ | 1866913811300089856 |
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| author | Morgillo, Angela Rosy Sacchi, Massimiliano F. Macchiavello, Chiara |
| author_facet | Morgillo, Angela Rosy Sacchi, Massimiliano F. Macchiavello, Chiara |
| contents | We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Markovianity of Quantum Processes via Recurrent Neural Networks Morgillo, Angela Rosy Sacchi, Massimiliano F. Macchiavello, Chiara Quantum Physics We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes. |
| title | Detecting Markovianity of Quantum Processes via Recurrent Neural Networks |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2406.07226 |