The most important characteristic of a Quantum Key Distribution (QKD) protocol is its security against third-party attacks, and the potential countermeasures available. While new types of attacks are regularly developed in the literature, they rarely involve the use of weak continuous measurement and more specifically machine learning to infer the qubit states. In this paper, we design a new individual attack scheme called \textit{Deep-learning-based continuous attack} (DLCA) that exploits continuous measurement together with the powerful pattern recognition capacities of deep recurrent neural networks. As a minimal model, we present its performances when applied in the case of the BB84 protocol with intrinsic noise in the communication channel. Our results suggest that our attack's performances lie between the ones of standard intercept-and-resend attacks and of the optimal individual attack, namely the phase-covariant quantum cloner. Our attack scheme demonstrates deep-learning-enhanced quantum state tomography applied to QKD, and could be generalized in many different ways, notably in the cases of quantum hacking attacks targeting implementation vulnerabilities that could compromise the security of QKD protocols.
The most important characteristic of a Quantum Key Distribution (QKD) protocol is its security against third-party attacks, and the potential countermeasures available. While new types of attacks are regularly developed in the literature, they rarely involve the use of weak continuous measurement and more specifically machine learning to infer the qubit states. In this paper, we design a new individual attack scheme called \textit{Deep-learning-based continuous attack} (DLCA) that exploits continuous measurement together with the powerful pattern recognition capacities of deep recurrent neural networks. As a minimal model, we present its performances when applied in the case of the BB84 protocol with intrinsic noise in the communication channel. Our results suggest that our attack's performances lie between the ones of standard intercept-and-resend attacks and of the optimal individual attack, namely the phase-covariant quantum cloner. Our attack scheme demonstrates deep-learning-enhanced quantum state tomography applied to QKD, and could be generalized in many different ways, notably in the cases of quantum hacking attacks targeting implementation vulnerabilities that could compromise the security of QKD protocols.
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.
Removing quantum hacking attacks is a highly challenging task for quantum key distribution (QKD) since one needs to correctly detect the types of attacks. There is a big gap between the idealized theoretical model and the practical physical system, which results in various hacking strategies exploited from security loopholes. To bridge this gap, previous continuous-variable (CV) QKD systems adopted a universal statistical model to represent different dimensional characters of the physical layer. The challenge is to discriminate different attacks from the exposed key physical details. Here we present a semisupervised deep-learning method to detect the known attacks or potential threats that are hard to be captured in previous CVQKD systems. The proof-of-principle experiment result shows that it could break the present limitations that different attacks are relatively independent, and key features computed from previous threshold-based methods are not correlated with each other. We anticipate that our proposal will give insight into how quantum hacking attacks can be detected under the real system modeling constraints, and will enable the present quantum communication systems to reach a higher level of practical security without adding new assumptions.
In the contemporary quantum world, all the things depend on upcoming quantum technology because of its effectiveness and robustness on deploying it in certain devices. An important use of quantum cryptography is quantum key distribution (QKD), which promises strong security by utilising quantum mechanical properties. However, real-world attack strategies have created practical vulnerabilities. The practical security analysis of intercept-and-resend attacks over continuous variable quantum key distribution (CV-QKD) and discrete variable quantum key distribution (DV-QKD) protocols is investigated in this work. The simulation models can be developed using custom NumPy and QuTip, we systematically analyze the protocols performance under changing channel losses and attack probabilities for the successful emulation of quantum communication processes. In order to evaluate protocol robustness, our study assesses key security measures such as Key generation rate and channel loss exemplified by simulation over QKD protocols. Our simulation testbed shows the sensitivity of the protocols to Intercept-and-Resend (IR) attacks and recognize under various environments where the security premises breakdown. The main objective of this work is to bridge the research gap between quantum theoretic security and its deployment in real-world communication networks, provides a clear insight into the design and development of more sophisticated quantum secure framework.
Quantum key distribution (QKD) is widely regarded as a highly secure quantum encryption approach since it applies theoretical principles of quantum mechanics as a medium of security, unlike classical encryption processes that rely on complex mathematics. However, practical QKD systems are vulnerable due to imperfect photon source hardware, communication channel, and detector implementation. These hardware imperfections open side channels that an adversary can exploit without violating principles of quantum mechanics. This review provides a comprehensive overview of the current QKD protocols, quantum hacking strategies and their operating principles, vulnerable hardware components and their mitigation techniques. This paper further investigates how state-of-the-art paradigms based on artificial intelligence (AI) and machine learning (ML), along with key subfields—such as deep learning (DL), deep reinforcement learning (DRL), and quantum machine learning (QML)—are reshaping the domain. In particular, this paper analyzes their dual roles as enablers of sophisticated attacks and as instruments for attack detection and mitigation. By jointly addressing the challenges in quantum communication and adaptive learning, as well as attack and detection architectures, this survey elucidates both the capabilities and the limitations of contemporary attack-detection mechanisms in the field. This survey seeks to establish a unified framework that conceptually integrates physical-layer quantum hacking with AI-/ML-based security, emphasizing their dual function as both an intelligent defensive layer and a potential tool for advanced threats.
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.
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.