# Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication

arXiv: https://arxiv.org/abs/2301.02609 · HTML: https://mlsanity.com/paper/2301.02609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning](https://mlsanity.com/paper/2506.19275/) (2025)

## Abstract (arXiv)

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.