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Free-space optical communications systems suffer from turbulent propagation of light through the atmosphere, attenuation, and receiver detector noise. These effects degrade the quality of the received state, increase cross-talk, and decrease symbol classification accuracy. We develop a state-of-the-art generative neural network (GNN) and convolutional neural network (CNN) system in combination, and demonstrate its efficacy in simulated and experimental communications settings. Experimentally, the GNN system corrects for distortion and reduces detector noise, resulting in nearly identical-to-desired mode profiles at the receiver, requiring no feedback or adaptive optics. Classification accuracy is significantly improved when these generated modes are demodulated using a CNN that is pre-trained with undistorted modes. Using the GNN and CNN system exclusively pre-trained with simulated optical profiles, we show a reduction in cross-talk between experimentally-detected noisy/distorted modes at the receiver. This scalable scheme may provide a concrete and effective demodulation technique for establishing long-range classical and quantum communication links.

Phase-sensitive nonlinear gain processes have been implemented as noise-reduced optical amplifiers, which have the potential to achieve signal-to-noise ratios beyond the classical limit. We experimentally demonstrate a novel phase-sensitive four-wave mixing amplification process in a single atomic vapor cell with only two input frequencies and two input vacuum modes. The amount of phase sensitivity depends on the power ratio between the inserted probes as well as on the input frequency of the probes. We find that, for certain phase values, the intensity noise of an output mode is lower than that of its phase-insensitive counterpart.