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IEEE India Conference

A New Error Correction Technique in Quantum Cryptography using Artificial Neural Networks

Gobinda Chandra Das, Malay Kule

Published Nov 24, 2022• 5 citations
Publisher

Abstract

Information Security is of paramount importance in digital communication in today’s highly networked world. Conventional information security methods face many future threats, one being the likelihood of large scale Quantum Computation. Quantum Cryptography is coming to the fore in the field of information security. It is all about using quantum physics to perform the cryptographic tasks as well as to break cryptographic systems. There are recent many advancements in the quantum cryptography field like Quantum Coin Flipping, Quantum Bit Commitment, and Quantum Key Distribution (QKD) networks. In this paper, a new concept for error correction and key reconciliation method is presented that uses Artificial Neural Network (ANN) with the help of Tree Parity Machine (TPM). In our work, we have considered a few cases of TPMs synchronization process with specific values of Quantum Bit Error Rate(QBER) and have performed some comparison among those. The experimental results observed into this paper that prove the correctness of the proposed work.

DOI
10.1109/indicon56171.2022.10040091

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Information Security is of paramount importance in digital communication in today’s highly networked world. Conventional information security methods face many future threats, one being the likelihood of large scale Quantum Computation. Quantum Cryptography is coming to the fore in the field of information security. It is all about using quantum physics to perform the cryptographic tasks as well as to break cryptographic systems. There are recent many advancements in the quantum cryptography field like Quantum Coin Flipping, Quantum Bit Commitment, and Quantum Key Distribution (QKD) networks. In this paper, a new concept for error correction and key reconciliation method is presented that uses Artificial Neural Network (ANN) with the help of Tree Parity Machine (TPM). In our work, we have considered a few cases of TPMs synchronization process with specific values of Quantum Bit Error Rate(QBER) and have performed some comparison among those. The experimental results observed into this paper that prove the correctness of the proposed work.
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.
The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to study how the number of synchronization iterations and the amount of leaked information depend on the quantum bit error rate (QBER) and the range of neural network weights. The results show a direct relationship between the average number of synchronization iterations and QBER, an increase in iterations when the weight range is expanded, and a reduction in leaked information as the weight range increases. Based on these results, conclusions are drawn regarding the applicability of the protocol and the prospects for further research on neural cryptographic methods in the context of key reconciliation.
Quantum key distribution is a promising technology for ensuring secure communication in the era of quantum computing. However, its practical implementation faces challenges related to error correction due to the imperfections of quantum channels. Recently, artificial neural networks have shown significant potential for enhancing this process. This paper introduces a simulation tool for analyzing the security and efficiency of artificial neural networks in correcting errors within quantum key distribution systems. The tool explores various configurations, error levels, and synchronization dynamics, providing insights into the iteration requirements for legitimate parties compared to an attacker attempting to interfere. The simulator’s flexibility allows for the implementation of alternative learning algorithms and input types, offering a foundation for advancing neural network-based error correction in quantum cryptography. Additionally, the study highlights the impact of quantum bit error rates and error types on correction performance.
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.
Quantum key distribution (QKD) is a secure communication technique that uses quantum mechanical principles. This method enables two trusted users to exchange a secret key or message securely by exploiting the fundamental properties of quantum mechanics. Strong long-distance secure communication is provided by QKD but its effectiveness is mainly limited by quantum bit error rate (QBER), finite-key effects, and the effectiveness of classical error-correction during information reconciliation. In this work, we proposes a neural-aided adaptive reconciliation (NAAR) framework based on artificial neural networks for QKD that integrates a lightweight neural belief propagation decoder with QBER-driven adaptive LDPC coderate selection. By lowering frame-error rate and information leakage, neural-assisted log-likelihood ratio calibration enhances belief-propagation convergence for binary symmetric channels. The result shows an improvement in secret key rates (SKR) and lower error rates over traditional LDPC-based reconciliation.
Error correction in quantum cryptography based on artificial neural networks is a new and promising solution. In this paper the security verification of this method is discussed and results of many simulations with different parameters are presented. The test scenarios assumed partially synchronized neural networks, typical for error rates in quantum cryptography. The results were also compared with scenarios based on the neural networks with random chosen weights to show the difficulty of passive attacks.