Phase correction using deep learning for satellite-to-ground CV-QKD

Nathan K. Long; Robert Malaney; Kenneth J. Grant

doi:10.1117/12.3018524

2 August 2024 Phase correction using deep learning for satellite-to-ground CV-QKD

Nathan K. Long, Robert Malaney, Kenneth J. Grant

Author Affiliations +

Proceedings Volume 13106, Photonics for Quantum 2024; 1310602 (2024) https://doi.org/10.1117/12.3018524
Event: Photonics for Quantum, 2024, Waterloo, Ontario, Canada

Abstract

Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. We show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nathan K. Long, Robert Malaney, and Kenneth J. Grant "Phase correction using deep learning for satellite-to-ground CV-QKD", Proc. SPIE 13106, Photonics for Quantum 2024, 1310602 (2 August 2024); https://doi.org/10.1117/12.3018524

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