Fast surrogate modelling of EIT in atomic quantum systems using LSTM neural networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914072490934272 |
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| author | Hita, Isabel S. Burdon Iglesias-González, Óscar Carral, Gabriel M. Ferreira-Cao, Miguel |
| author_facet | Hita, Isabel S. Burdon Iglesias-González, Óscar Carral, Gabriel M. Ferreira-Cao, Miguel |
| contents | Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter estimation, or real-time feedback. To address this challenge, we develop a Long Short-Term Memory neural network capable of replicating the output of these simulations with high accuracy and significantly reduced computational cost. Once trained, the surrogate model produces spectra in milliseconds, providing a speed-up of 5000x relative to traditional numerical solvers. We focus on applying this technique to Doppler-broadened Electromagnetically Induced Transparency in a ladder-type scheme for Rydberg-based sensing, achieving near-unity agreement with the physics solver for resonant and off-resonant regimes. We demonstrate the effectiveness of the LSTM model on this representative optical quantum system, establishing it as a surrogate tool capable of supporting real-time signal processing and feedback-based optimisation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast surrogate modelling of EIT in atomic quantum systems using LSTM neural networks Hita, Isabel S. Burdon Iglesias-González, Óscar Carral, Gabriel M. Ferreira-Cao, Miguel Atomic Physics Mathematical Physics Computational Physics Quantum Physics Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter estimation, or real-time feedback. To address this challenge, we develop a Long Short-Term Memory neural network capable of replicating the output of these simulations with high accuracy and significantly reduced computational cost. Once trained, the surrogate model produces spectra in milliseconds, providing a speed-up of 5000x relative to traditional numerical solvers. We focus on applying this technique to Doppler-broadened Electromagnetically Induced Transparency in a ladder-type scheme for Rydberg-based sensing, achieving near-unity agreement with the physics solver for resonant and off-resonant regimes. We demonstrate the effectiveness of the LSTM model on this representative optical quantum system, establishing it as a surrogate tool capable of supporting real-time signal processing and feedback-based optimisation. |
| title | Fast surrogate modelling of EIT in atomic quantum systems using LSTM neural networks |
| topic | Atomic Physics Mathematical Physics Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2510.02603 |