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Predicting atmospheric turbulence for secure quantum communications in free space
Optics Express
|August 13, 2025
Summary
We developed TAROQQO, a recurrent neural network, to predict atmospheric turbulence for quantum communication. Accurate turbulence forecasting enables optimal timing for secure free-space quantum key distribution links.
Area of Science:
- Quantum communication
- Atmospheric physics
- Machine learning
Background:
- Atmospheric turbulence hinders large-scale free-space quantum communication by distorting optical signals.
- Predicting turbulence strength is crucial for establishing secure quantum links by optimizing transmission timing.
Purpose of the Study:
- To develop and train a recurrent neural network (TAROQQO) for forecasting atmospheric turbulence strength.
- To assess the impact of turbulence prediction on quantum key distribution (QKD) protocols.
Main Methods:
- A recurrent neural network, TAROQQO, was trained using 9 months of weather and turbulence data from a 5.4 km intra-city free-space link.
- Simulations of a high-dimensional QKD protocol using orbital angular momentum states were performed under various turbulence regimes.
Main Results:
- TAROQQO demonstrated the ability to predict turbulence strength in a free-space optical channel.
- Accurate turbulence predictions were shown to be beneficial for a simulated QKD protocol, especially in challenging turbulence conditions.
Conclusions:
- TAROQQO is a valuable tool for validating free-space channels and optimizing secure communication link establishment.
- Accurate turbulence forecasting is essential for the practical implementation of large-scale free-space quantum networks.
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