Predicting atmospheric turbulence for secure quantum communications in free space

• URL: http://arxiv.org/abs/2406.14768v1
• Date: Thu, 20 Jun 2024 22:38:13 GMT
• Title: Predicting atmospheric turbulence for secure quantum communications in free space
• Authors: Tareq Jaouni, Lukas Scarfe, Frédéric Bouchard, Mario Krenn, Khabat Heshami, Francesco Di Colandrea, Ebrahim Karimi,
• Abstract summary: We train a Recurrent Neural Network, TAROCCO, to predict the turbulence strength within a free-space channel. The training is based on weather and turbulence data collected over 9 months for a 5.4 km intra-city free-space link across the City of Ottawa.
• Score: 0.4517077427559345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Atmospheric turbulence is the main barrier to large-scale free-space quantum communication networks. Aberrations distort optical information carriers, thus limiting or preventing the possibility of establishing a secure link between two parties. For this reason, forecasting the turbulence strength within an optical channel is highly desirable, as it allows for knowing the optimal timing to establish a secure link in advance. Here, we train a Recurrent Neural Network, TAROCCO, to predict the turbulence strength within a free-space channel. The training is based on weather and turbulence data collected over 9 months for a 5.4 km intra-city free-space link across the City of Ottawa. The implications of accurate predictions from our network are demonstrated in a simulated high-dimensional Quantum Key Distribution protocol based on orbital angular momentum states of light across different turbulence regimes. TAROCCO will be crucial in validating a free-space channel to optimally route the key exchange for secure communications in real experimental scenarios.

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