Statistical verifications and deep-learning predictions for satellite-to-ground quantum atmospheric channels

• Published in: Communications physics Vol. 5; no. 1; pp. 1 - 18
• Main Authors: Trinh, Phuc V., Carrasco-Casado, Alberto, Takenaka, Hideki, Fujiwara, Mikio, Kitamura, Mitsuo, Sasaki, Masahide, Toyoshima, Morio
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12-09-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/624/1075/187; 639/624/400/482; Atmosphere; Atmospheric models; Atmospheric turbulence; Bit error rate; Channels; Communication; Communications technology; Cubesat; Deep learning; Error analysis; Ground stations; Information technology; Internet access; Low earth orbits; Microsatellites; Neural networks; Optical communication; Photons; Physics; Physics and Astronomy; Power; Quantum cryptography; Quantum mechanics; Qubits (quantum computing); Random variables; Recurrent neural networks; Satellites; Small satellites; Statistical models; Statistical prediction
• ISSN: 2399-3650; 2399-3650
• DOI: 10.1038/s42005-022-01002-1

Laser communications from small satellite platforms empowers the establishment of quantum key distribution (QKD), relying on quantum superposition states of single photons to realize unconditional security between distant parties at a global scale. Although recent breakthrough experiments have demonstrated the feasibility of satellite-to-ground QKD links, the underlying statistical characteristics of quantum atmospheric channels have not been well-understood and experimentally verified in the literature. In this paper, we highlight that classical atmospheric statistical models can be applied for describing random fluctuations of the quantum channels. To verify this fact, we report a statistical verification study of quantum atmospheric channels from the world’s first low-Earth orbit (LEO) 50-kg-class microsatellite-to-ground quantum-limited communication experiment. The verified statistical model is then applied to numerically investigate the quantum bit-error rate (QBER) and secret-key length (SKL) of a decoy-state efficient Bennett-Brassard 1984 (BB84) QKD protocol with optimized parameters considering finite-key effects, implemented over a LEO 6-unit (6U)-CubeSat-to-ground link. Important insights of the physical channel effects including pointing errors and atmospheric turbulence on the QBER and SKL are then revealed. Finally, we present a study using a deep-learning-based long short-term memory (LSTM) recurrent neural network (RNN) for predicting photon-count fluctuations over quantum atmospheric channels.
This study confirms that a classical channel model can be used for describing random fluctuations in LEO-to-ground quantum atmospheric channels. It shows that practical engineering designs for future QKD missions can be conveniently conducted using the verified channel model, and that deep learning can predict channel fluctuations.