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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| Published in: | 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) pp. 468 - 473 |
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| Main Authors: | , , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
01.12.2022
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Haga, Peter Bako, Bence Kallus, Zsofia Tabi, Zsolt Vaderna, Peter Zimboras, Zoltan Nagy, Daniel T. R. |
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| Snippet | Quantum neural networks are emerging as poten-tial candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid... |
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| SubjectTerms | Physical layer quantum autoencoder Quantum computing quantum machine learning radio communication Radio transmitters Receivers Robustness Symbols variational quantum algorithms Wireless communication |
| Title | Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication |
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