Error correction in quantum cryptography based on artificial neural networks
This addresses the challenge of secure and efficient key distillation in quantum key distribution, which is crucial for advancing quantum cryptography, though it appears incremental as it builds on neural cryptography concepts.
The paper tackles the problem of key reconciliation in quantum cryptography by proposing a method using mutual synchronization of artificial neural networks to correct errors in quantum channels, showing that the synchronization process is much faster than in neural cryptography, which enhances security against eavesdroppers.
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 problem 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.