Error correction in quantum cryptography based on artificial neural networks

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 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.

References in corpus (11)

• The Security of Practical Quantum Key Distribution
• Satellite-to-ground quantum key distribution
• Quantum cryptography protocols robust against photon number splitting attacks for weak laser pulses implementations
• Experimental Satellite Quantum Communications
• Fast, efficient error reconciliation for quantum cryptography
• Secure exchange of information by synchronization of neural networks
• Mutual learning in a tree parity machine and its application to cryptography
• Dynamics of neural cryptography
• A secure key-exchange protocol with an absence of injective functions
• Cryptography based on neural networks - analytical results
• Neural cryptography with queries

Cited by in corpus (6)

• Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
• Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning
• Asymmetric adaptive LDPC-based information reconciliation for industrial quantum key distribution
• Synchronization of Tree Parity Machines using non-binary input vectors
• The Transfer of Entanglement Negativity at the Onset of Interactions
• Secure synchronization of artificial neural networks used to correct errors in quantum cryptography