We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm.
We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm.
Many modern classical encryption schemes, based on computational complexity, are threatened by quantum computers. Quantum Key Distribution (QKD) aims to address this threat, with security enforced by quantum-mechanical principles. The security of QKD protocols, like the six-state protocol (6QKD), relies on understanding all possible eavesdropping attacks. The optimal known individual eavesdropping attack on 6QKD in a noiseless channel is the Universal Quantum Cloning Machine (UQCM). However, the utility of quantum machine learning (QML) for QKD eavesdropping analysis is largely unexplored. The recent work by Decker et al. (2025) used QML to find new attacks on the four-state BB84 protocol, but this methodology has not yet been generalized to the more complex 6QKD protocol. This thesis generalizes the quantum circuit (QCL) learning framework to analyze individual eavesdropping attacks on 6QKD. We designed a parameterized quantum circuit (ansatz) appropriate for 6QKD, notably adapting our circuit to two ancilla, and expanding the measurement strategy to three bases. We used QCL to find the optimal attack parameters in a simulated noiseless, asymmetric (bit-flip, phase-flip), and symmetric (depolarizing) noise channels. In the noiseless quantum channel, the QCL successfully converges to and reproduces the theoretical performance of the UQCM, validating the adapted methodology. In the asymmetric bit-flip and phase-flip noise channels, the QCL discovers a novel "imbalanced cloner" attack that outperforms the standard UQCM in average fidelity. The model learns to exploit the noise asymmetry by sacrificing the fidelity in the degraded bases and gaining higher fidelity in the unaffected basis. In a symmetric depolarizing noise channel, the QCL performance converges exactly to the UQCM, demonstrating it finds no exploitable noise asymmetry. This work provides the first QCL-based security analysis of the 6QKD protocol. We demonstrate that the QCL is a generalizable tool for quantum security analysis. Furthermore, we present the first (to our knowledge) evidence of a 6QKD "imbalanced cloner" that outperforms UQCM in asymmetric noisy environments.
Quantum Key Distribution (QKD) promises theoretically unbreakable encryption by using fundamental quantum physics principles. QKD has emerged as a contender in the next generation of encryption protocols as the demand for more secure communication grows in our linked society. However, practical QKD implementations confront obstacles such as noise, interception attempts, and flaws in quantum hardware. We investigate the possibilities of Machine Learning (ML) techniques to improve the efficiency, resilience, and security of QKD protocols in this study. We provide a novel system that uses deep learning models to optimize photon polarization states and hence increase the key generation rate. Our approach detects and mitigates eavesdropping attempts using unsupervised learning algorithms, substantially enhancing the security of quantum communications. To achieve security, the BB84 protocol makes use of fundamental quantum mechanics features. Furthermore, we use reinforcement learning to adaptively alter system settings in real time to account for the quirks and noise profiles of different quantum channels. In comparison to typical QKD setups, experimental results show that our ML-enhanced QKD system is more resilient to known quantum attacks and provides a more consistent and efficient key distribution rate. Our findings indicate that the interaction of quantum physics and machine learning can pave the way for more secure and practical quantum communication networks in the near future.
ABSTRACT The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public‐key cryptographic algorithms. Quantum key distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as photon number splitting and Trojan‐Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect popular QKD attacks. In particular, we propose a hybrid quantum long short‐term memory (QLSTM) model to improve the detection of common QKD attacks. By combining quantum‐enhanced learning with classical deep learning, the model captures complex temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a realistic QKD dataset that simulates typical QKD operations, along with seven attack scenarios: Intercept‐and‐resend, photon‐number splitting (PNS), Trojan‐Horse attacks on the random number generator (RNG), detector blinding, wavelength‐dependent Trojan‐Horse and combined attacks. The dataset includes quantum security metrics such as quantum bit error rate (QBER), measurement entropy, signal and decoy loss rates and time‐based metrics, ensuring an accurate representation of real‐world conditions. Our results demonstrate the promising performance of the quantum machine learning approach compared to traditional classical machine learning models, highlighting the potential of hybrid techniques to enhance the security of future quantum communication networks. The proposed hybrid QLSTM model achieved an accuracy of 94.7.0% after 50 training epochs, outperforming classical deep learning models such as LSTM, CNN, ANN, random forest and RNN. However, our evaluation is conducted on a semi‐realistic, simulation‐generated decoy‐state BB84 dataset with simplified models of attacks and device imperfections, so the reported performance should be interpreted as a proof‐of‐concept rather than a final assessment on field‐deployed QKD systems.
Quantum key distribution (QKD) has emerged as a critical component of secure communication in the quantum era, ensuring information-theoretic security. Despite its potential, there are issues in optimizing key generation rates, enhancing security, and incorporating QKD into practical implementations. This research introduces a unique framework for incorporating quantum machine learning (QML) algorithms, notably quantum reinforcement learning (QRL) and quantum neural networks (QNN), into QKD protocols to improve key generation performance. Here, we present two novel QRL-based algorithms, QRL-V.1 and QRL-V.2, and propose the standard BB84 and B92 protocols by integrating QNN algorithms to form QNN-BB84 and QNN-B92. Furthermore, we combine QNN with the above QRL-based algorithms to produce QNN-QRL-V.1 and QNN-QRL-V.2. These unique algorithms and established protocols are compared using evaluation metrics such as accuracy, precision, recall, F1 score, confusion matrices, and ROC curves. The results from the QNN-based proposed algorithms show considerable improvements in key generation quality. The existing and proposed models are investigated in the presence of different noisy channels to check their robustness. The proposed integration of QML algorithms into QKD protocols and their noisy analysis create a new paradigm for efficient key generation, which advances the practical implementation of QKD systems.
The rapid development of quantum computing threatens the traditional cryptographic schemes and suggests that the best alternative solution for ensuring the security of key exchange is Quantum Key Distribution (QKD).Despite the information-theoretic security guarantee, the practical implementations of QKD present vulnerabilities that were derived from hardware flaws and the exploitation of the quantum channel.This thesis is intended to address security vulnerabilities by analyzing and proposing an innovative architecture for the identification and mitigation of quantum attacks, via classification techniques of Quantum Machine Learning and the use of the metrics of Quantitative Risk Analysis.The study begins with the concepts of quantum information, the functionality of basic QKD protocols (BB84, B92, E91) and the mathematical modeling of Implementation-level attacks and side-channel attacks in quantum systems.Subsequently, the operational processes of Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC) are analyzed during the training phase.Based on the theoretical background of this study, QML-IDPS architecture is proposed.Threat detection and classification are performed using the QSVM and VQC classifiers, while risk metrics are utilized and fed into a classical Intrusion Prevention System (IPS) to support the final decision-making process for the prevention of quantum threats.The final key validated by the threat assessment mechanisms, is then used for data encryption with a classical cryptographic algorithm, such as AES-256.The proper functionality of QML-IDPS architecture is verified with the mathematical Intercept-Resend Use Case Scenario, which basically proves the successful classification and prevention of system anomalies.In conclusion, it is confirmed that the proposed QML-IDPS architecture demonstrates that Quantum Machine Learning combined with Quantitative Risk Analysis can enhance the security and reliability of QKD infrastructures.
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.