This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms.
This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms.
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.
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 emerged from the limitations of classical cryptography. It will play a vital role in information security after the availability of expected powerful quantum computers. Still many quantum primitives like quantum money, blind quantum computation, quantum copy protection, etc. are theoretical as they require a completely functional quantum computer for their implementation. But one prominent quantum cryptographic primitive, the Quantum Key Distribution (QKD) is possible with current technology. The QKD is a key establishment system having several stages namely raw key generation, key sifting, key reconciliation, and privacy amplification. In this paper, an efficient key sifting scheme has been developed. Successful simulation has shown that the proposed modified key sifting scheme requires less time to build the sifted key compared to the sifted key in conventional BB84 protocol in most cases. This paper also represents Tree Parity Machine (TPM) based key reconciliation analysis using different learning algorithms such as Hebbian, Anti-Hebbian, and Random-Walk. This reconciliation analysis helps to choose the optimum learning algorithm for Artificial Neural Network (ANN) based key reconciliation in future Quantum Key Distribution systems.
Quantum Key Distribution (QKD) is a secure communication method that uses quantum mechanics to exchange secret keys. Currently, the QKD process uses a static error correction method, which can lead to inaccuracies and inefficient resource use. The project aims to develop a new QKD error correction method that adapts dynamically based on real-time Quantum Bit Error Rate (QBER) measurements and Software Defined Networking (SDN) principles. Two specific error correction mechanisms, the 2D Parity check and the Cascade Protocol, will be used to match the QBER value. The investigation will be conducted using the DARPAN application, providing a quantum-enabled environment.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.
Abstract The secure distribution of keys is essential to ensuring the privacy and integrity of data in Internet of Things (IoT) systems. Quantum Key Distribution (QKD) protocols, such as BB84, use principles from quantum mechanics to establish secure communication channels. However, errors that occur during the transmission of qubits can compromise the reliability of the shared key. This paper presents a new method for error correction that integrates Convolutional Neural Networks (CNNs) into the BB84 protocol for QKD in IoT systems. By harnessing the exceptional pattern recognition capabilities of CNNs, our approach learns hierarchical representations from preprocessed quantum channel data to effectively identify and correct errors. Extensive simulations demonstrate the high performance of our CNN-based method in reducing the Quantum Bit Error Rate (QBER). This contribution significantly enhances the security and reliability of key distribution, offering a promising solution for robust quantum communication in IoT environments.