Supervised learning of the Jaynes–Cummings Hamiltonian
Abstract We investigate the utility of deep neural networks (DNNs) in estimating the Jaynes-Cummings Hamiltonian’s parameters from its energy spectrum alone. We assume that the energy spectrum may or may not be corrupted by noise. In the noiseless case, we use the vanilla DNN (vDNN) model and find t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-02611-w |
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| author | Woohyun Choi Chang-Woo Lee Changsuk Noh |
| author_facet | Woohyun Choi Chang-Woo Lee Changsuk Noh |
| author_sort | Woohyun Choi |
| collection | DOAJ |
| description | Abstract We investigate the utility of deep neural networks (DNNs) in estimating the Jaynes-Cummings Hamiltonian’s parameters from its energy spectrum alone. We assume that the energy spectrum may or may not be corrupted by noise. In the noiseless case, we use the vanilla DNN (vDNN) model and find that the error tends to decrease as the number of input nodes increases. The best-achieved root mean squared error is of the order of $$10^{-5}$$ . The vDNN model, trained on noiseless data, demonstrates resilience to Gaussian noise, but only up to a certain extent. To cope with this issue, we employ a denoising U-Net and combine it with the vDNN to find that the new model reduces the error by up to about 77%. Our study exemplifies that deep learning models can help estimate the parameters of a Hamiltonian even when the data is corrupted by noise. |
| format | Article |
| id | doaj-art-3cd14c6147d849f4b8f93b18dfb2f483 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3cd14c6147d849f4b8f93b18dfb2f4832025-08-20T03:05:17ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-02611-wSupervised learning of the Jaynes–Cummings HamiltonianWoohyun Choi0Chang-Woo Lee1Changsuk Noh2Kyungpook National UniversityKorea Institute for Advanced StudyKyungpook National UniversityAbstract We investigate the utility of deep neural networks (DNNs) in estimating the Jaynes-Cummings Hamiltonian’s parameters from its energy spectrum alone. We assume that the energy spectrum may or may not be corrupted by noise. In the noiseless case, we use the vanilla DNN (vDNN) model and find that the error tends to decrease as the number of input nodes increases. The best-achieved root mean squared error is of the order of $$10^{-5}$$ . The vDNN model, trained on noiseless data, demonstrates resilience to Gaussian noise, but only up to a certain extent. To cope with this issue, we employ a denoising U-Net and combine it with the vDNN to find that the new model reduces the error by up to about 77%. Our study exemplifies that deep learning models can help estimate the parameters of a Hamiltonian even when the data is corrupted by noise.https://doi.org/10.1038/s41598-025-02611-w |
| spellingShingle | Woohyun Choi Chang-Woo Lee Changsuk Noh Supervised learning of the Jaynes–Cummings Hamiltonian Scientific Reports |
| title | Supervised learning of the Jaynes–Cummings Hamiltonian |
| title_full | Supervised learning of the Jaynes–Cummings Hamiltonian |
| title_fullStr | Supervised learning of the Jaynes–Cummings Hamiltonian |
| title_full_unstemmed | Supervised learning of the Jaynes–Cummings Hamiltonian |
| title_short | Supervised learning of the Jaynes–Cummings Hamiltonian |
| title_sort | supervised learning of the jaynes cummings hamiltonian |
| url | https://doi.org/10.1038/s41598-025-02611-w |
| work_keys_str_mv | AT woohyunchoi supervisedlearningofthejaynescummingshamiltonian AT changwoolee supervisedlearningofthejaynescummingshamiltonian AT changsuknoh supervisedlearningofthejaynescummingshamiltonian |