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arXiv · 2301.02609

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

Zsolt Tabi·Bence Bakó·Dániel T. R. Nagy·Péter Vaderna·Zsófia Kallus·Péter Hága·Zoltán Zimborás
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Abstract

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.

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Zsolt Tabi, Bence Bakó, Dániel T. R. Nagy, Péter Vaderna, Zsófia Kallus, Péter Hága, Zoltán Zimborás. 2023-01-06. Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication. https://doi.org/10.1109/sec54971.2022.00071

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