Channel-noise tracking for sub-shot-noise-limited receivers with neural networks
February 24, 2021
(a) A sender (Alice) and receiver (Bob) attempt to communicate across a noisy channel. Bob implements a state discrimination measurement based on photon counting to decode the information sent by Alice, and uses a neural network to track and correct for channel noise. (b) Example of the error probability as a function of time when applying phase and amplitude noise to the input states. The noise tracking algorithm based on a neural network estimator (blue) achieves equivalent performance to a strategy based on a far more computationally expensive Bayesian estimator (orange).