Szczegóły publikacji
Opis bibliograficzny
Secure synchronization of artificial neural networks used to correct errors in quantum cryptography / Marcin NIEMIEC, Tymoteusz Widlarz, Miralem Mehic // W: IEEE ICC 2023 [Dokument elektroniczny] : IEEE International Conference on Communications : 28 May–1 June 2023, Rome, Italy / ed. by Michele Zorzi, Meixia Tao, Walid Saad. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, 2023. — (IEEE International Conference on Communications ; ISSN 1938-1883). — Dod. ISBN: 978-1-5386-7463-5. — e-ISBN: 978-1-5386-7462-8. — S. 3491–3496. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 3496, Abstr. — Publikacja dostępna online od: 2023-10-23
Autorzy (3)
- AGHNiemiec Marcin
- AGHWidlarz Tymoteusz
- Mehic Miralem
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150546 |
|---|---|
| Data dodania do BaDAP | 2024-01-08 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICC45041.2023.10279837 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Communications |
| Czasopismo/seria | IEEE International Conference on Communications |
Abstract
Quantum cryptography can provide a very high level of data security. However, a big challenge of this technique is errors in quantum channels. Therefore, error correction methods must be applied in real implementations. An example is error correction based on artificial neural networks. This paper considers the practical aspects of this recently proposed method and analyzes elements which influence security and efficiency. The synchronization process based on mutual learning processes is analyzed in detail. The results allowed us to determine the impact of various parameters. Additionally, the paper describes the recommended number of iterations for different structures of artificial neural networks and various error rates. All this aims to support users in choosing a suitable configuration of neural networks used to correct errors in a secure and efficient way.