  Uncertainty in discriminating between different received coherent signals is
integral to the operation of many free-space optical communications protocols,
and is often difficult when the receiver measures a weak signal. Here we design
an optical communications scheme that uses balanced homodyne detection in
combination with an unsupervised generative machine learning and convolutional
neural network (CNN) system, and demonstrate its efficacy in a realistic
simulated coherent quadrature phase shift keyed (QPSK) communications system.
Additionally, we program the neural network system at the transmitter such that
it autonomously learns to correct for the noise associated with a weak QPSK
signal, which is shared with the network state of the receiver prior to the
implementation of the communications. We find that the scheme significantly
reduces the overall error probability of the communications system, achieving
the classical optimal limit. This communications design is straightforward to
build, implement, and scale. We anticipate that these results will allow for a
significant enhancement of current classical and quantum coherent optical
communications technologies.
