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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| Vydané v: | 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) s. 468 - 473 |
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| Hlavní autori: | , , , , , , |
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| Jazyk: | English |
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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. |
| Author_xml | – sequence: 1 givenname: Zsolt surname: Tabi fullname: Tabi, Zsolt email: zsolttabi@ik.elte.hu organization: Eötvös Loránd University,Budapest,Hungary – sequence: 2 givenname: Bence surname: Bako fullname: Bako, Bence email: bako.bence@wigner.hu organization: Eötvös Loránd University,Budapest,Hungary – sequence: 3 givenname: Daniel T. R. surname: Nagy fullname: Nagy, Daniel T. R. email: daniel.a.nagy@ericsson.com organization: Eötvös Loránd University,Budapest,Hungary – sequence: 4 givenname: Peter surname: Vaderna fullname: Vaderna, Peter email: peter.vaderna@ericsson.com organization: Ericsson Research,Budapest,Hungary – sequence: 5 givenname: Zsofia surname: Kallus fullname: Kallus, Zsofia email: zsofia.kallus@ericsson.com organization: Ericsson Research,Budapest,Hungary – sequence: 6 givenname: Peter surname: Haga fullname: Haga, Peter email: peter.haga@ericsson.com organization: Ericsson Research,Budapest,Hungary – sequence: 7 givenname: Zoltan surname: Zimboras fullname: Zimboras, Zoltan email: zimboras.zoltan@wigner.hu organization: Eötvös Loránd University,Budapest,Hungary |
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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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