Phase Correction using Deep Learning for Satellite-to-Ground CV-QKD
Nathan K. Long Robert Malaney Kenneth J. Grant
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. Conventional
wisdom dictates such an approach would likely be fruitless. However, 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.