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

Quantum neural networks are emerging as poten-tial candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical au-to encoders for end-to-end radio communication. In the physical layer of classical wireless systems, we study the performance of si...

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Bibliographic Details
Published in:2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) pp. 468 - 473
Main Authors: Tabi, Zsolt, Bako, Bence, Nagy, Daniel T. R., Vaderna, Peter, Kallus, Zsofia, Haga, Peter, Zimboras, Zoltan
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2022
Subjects:
Physical layer
quantum autoencoder
Quantum computing
quantum machine learning
radio communication
Radio transmitters
Receivers
Robustness
Symbols
variational quantum algorithms
Wireless communication
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Summary:Quantum neural networks are emerging as poten-tial candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical au-to encoders 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.
DOI:10.1109/SEC54971.2022.00071