Szczegóły publikacji

Opis bibliograficzny

Error correction in quantum cryptography based on artificial neural networks / Marcin NIEMIEC // Quantum Information Processing ; ISSN 1570-0755. — 2019 — vol. 18 iss. 6 art. no. 174, s. 1–18. — Bibliogr. s. 16–18, Abstr. — Publikacja dostępna online od: 2019-04-25


Autor


Słowa kluczowe

error correctionartificial neural networksmachine learningquantum cryptography

Dane bibliometryczne

ID BaDAP123014
Data dodania do BaDAP2019-07-25
Tekst źródłowyURL
DOI10.1007/s11128-019-2296-4
Rok publikacji2019
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaQuantum Information Processing

Abstract

Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problems remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent; therefore, the strings are similar and also users' neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.

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Postquantum symmetric cryptography inspired by neural networks / Wojciech Węgrzynek, Paweł TOPA // W: FedCSIS 2023 [Dokument elektroniczny] : proceedings of the 18th conference on Computer Science and Intelligence Systems : September 17–20, 2023, Warsaw, Poland / eds. Maria Ganzha, [et al.]. — Wersja do Windows. — Dane tekstowe. — Warszawa : Polskie Towarzystwo Informatyczne, cop. 2023. — (Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; Vol. 35). — Dod.: ART: ISBN 978-83-969601-0-8, USB: ISBN 978-83-967447-9-1. — e-ISBN: 978-83-967447-8-4. — S. 1205–1210. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://annals-csis.org/proceedings/2023/drp/pdf/9901.pdf [2023-10-06]. — Bibliogr. s. 1210, Abstr.