Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication
End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluat...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10981758/ |
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| author | Zsolt I. Tabi Bence Bako Daniel T. R. Nagy Peter Vaderna Zsofia Kallus Peter Haga Zoltan Zimboras |
| author_facet | Zsolt I. Tabi Bence Bako Daniel T. R. Nagy Peter Vaderna Zsofia Kallus Peter Haga Zoltan Zimboras |
| author_sort | Zsolt I. Tabi |
| collection | DOAJ |
| description | End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints – including data re-uploading methods, encoding schemes, and core layer structures. |
| format | Article |
| id | doaj-art-9ea1a04e0c2147e49ab3c314e82c0240 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9ea1a04e0c2147e49ab3c314e82c02402025-08-20T02:31:02ZengIEEEIEEE Access2169-35362025-01-0113821818219210.1109/ACCESS.2025.356620710981758Quantum-Classical Autoencoder Architectures for End-to-End Radio CommunicationZsolt I. Tabi0https://orcid.org/0000-0003-2070-7452Bence Bako1https://orcid.org/0009-0004-8756-7890Daniel T. R. Nagy2Peter Vaderna3https://orcid.org/0000-0003-1813-1562Zsofia Kallus4https://orcid.org/0000-0002-5270-1891Peter Haga5Zoltan Zimboras6Faculty of Informatics, Eötvös Loránd University, Budapest, HungaryFaculty of Informatics, Eötvös Loránd University, Budapest, HungaryQuantum Computing and Information Group, HUN-REN Wigner Research Centre for Physics, Budapest, HungaryEricsson Research, Budapest, HungaryEricsson Research, Budapest, HungaryEricsson Research, Budapest, HungaryFaculty of Informatics, Eötvös Loránd University, Budapest, HungaryEnd-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints – including data re-uploading methods, encoding schemes, and core layer structures.https://ieeexplore.ieee.org/document/10981758/Quantum machine learningquantum autoencoderradio communication |
| spellingShingle | Zsolt I. Tabi Bence Bako Daniel T. R. Nagy Peter Vaderna Zsofia Kallus Peter Haga Zoltan Zimboras Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication IEEE Access Quantum machine learning quantum autoencoder radio communication |
| title | Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication |
| title_full | Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication |
| title_fullStr | Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication |
| title_full_unstemmed | Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication |
| title_short | Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication |
| title_sort | quantum classical autoencoder architectures for end to end radio communication |
| topic | Quantum machine learning quantum autoencoder radio communication |
| url | https://ieeexplore.ieee.org/document/10981758/ |
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