Secure Synchronization of Artificial Neural Networks Used to Correct Errors in Quantum Cryptography
- Resource Type
- Conference
- Authors
- Niemiec, Marcin; Widlarz, Tymoteusz; Mehic, Miralem
- Source
- ICC 2023 - IEEE International Conference on Communications Communications, ICC 2023 - IEEE International Conference on. :3491-3496 May, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Error analysis
Quantum channels
Artificial neural networks
Error correction
Synchronization
Quantum cryptography
quantum cryptography
key reconciliation
error correction
artificial neural networks
- Language
- ISSN
- 1938-1883
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.