  Quantum neural networks are emerging as potential candidates to leverage
noisy quantum processing units for applications. Here we introduce hybrid
quantum-classical autoencoders for end-to-end radio communication. In the
physical layer of classical wireless systems, we study the performance of
simulated architectures for standard encoded radio signals over a noisy
channel. We implement a hybrid model, where a quantum decoder in the receiver
works with a classical encoder in the transmitter part. Besides learning a
latent space representation of the input symbols with good robustness against
signal degradation, a generalized data re-uploading scheme for the qubit-based
circuits allows to meet inference-time constraints of the application.
