Fast surrogate modelling of EIT in atomic quantum systems using LSTM neural networks

arXiv:2510.02603 · physics.atom-ph, math-ph, math.MP, physics.comp-ph, quant-ph · Submitted 2025-10-02 · Read on arXiv

physics.atom-ph, math-ph, math.MP, physics.comp-ph, quant-ph

Submitted: 2025-10-02

Updated: 2025-10-02

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Electromagnetically Induced Transparency — 6× in this paper
• Long Short-Term Memory — 4× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Optical Bloch Equations — 3× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Closed-Loop Control — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Dynamic Adaptation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Many-Body Effects — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• An Imaging Radar Using a Rydberg Atom Receiver
• High-Resolution Quantum Sensing with Rydberg Atomic Receiver: Principles, Experiments and Future Prospects
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics
• Chip-Scale Rydberg Atomic Electrometer
• MEMS Vapor Cells-based Rydberg-atom Electrometry Toward Miniaturization and High Sensitivity

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