The quantum Internet promises to revolutionize computing, communications, sensing, and security by leveraging quantum entanglement. In this context, quantum repeaters play a crucial role by performing entanglement swapping to extend entanglement across distant nodes. However, malicious repeaters can retain entangled pairs, enabling man-in-the-middle attacks that compromise security. Quantum Identity Authentication (QIA) protocols aim to mitigate this threat by verifying the legitimacy of communicating parties. However, existing QIA protocols rely on unrealistic assumptions, such as noise-free environments or static noise profiles, that do not reflect the nature of real-world quantum networks. Such noise can cause benign sessions to be flagged as attacks or allow attacks to go undetected. To address these limitations, we propose a machine learning-enhanced QIA framework designed for noisy quantum environments. The protocol performs random authentications during communication sessions using parameters derived from a shared secret key. These parameters determine the preparation and measurement of authentication qubits, known only to legitimate users. A trained machine learning model analyzes physical-layer information (e.g., entanglement fidelity, routing path) and protocol-level data (e.g., basis selection, encoding bit, and measurement outcomes) to detect malicious behavior under fluctuating noise conditions. We evaluate several attack detection models and identify XGBoost as the most effective, achieving 97.18% accuracy and an F1-score of 0.96. The proposed XGBoost-enhanced QIA protocol detects attacks within the first authentication round, achieving a 100% detection and a 1.7% false alarm rate. This reflects a 16.1% improvement in detection rate and a 92.5% reduction in false alarms compared to the entanglement-based QIA benchmark.
The quantum Internet promises to revolutionize computing, communications, sensing, and security by leveraging quantum entanglement. In this context, quantum repeaters play a crucial role by performing entanglement swapping to extend entanglement across distant nodes. However, malicious repeaters can retain entangled pairs, enabling man-in-the-middle attacks that compromise security. Quantum Identity Authentication (QIA) protocols aim to mitigate this threat by verifying the legitimacy of communicating parties. However, existing QIA protocols rely on unrealistic assumptions, such as noise-free environments or static noise profiles, that do not reflect the nature of real-world quantum networks. Such noise can cause benign sessions to be flagged as attacks or allow attacks to go undetected. To address these limitations, we propose a machine learning-enhanced QIA framework designed for noisy quantum environments. The protocol performs random authentications during communication sessions using parameters derived from a shared secret key. These parameters determine the preparation and measurement of authentication qubits, known only to legitimate users. A trained machine learning model analyzes physical-layer information (e.g., entanglement fidelity, routing path) and protocol-level data (e.g., basis selection, encoding bit, and measurement outcomes) to detect malicious behavior under fluctuating noise conditions. We evaluate several attack detection models and identify XGBoost as the most effective, achieving 97.18% accuracy and an F1-score of 0.96. The proposed XGBoost-enhanced QIA protocol detects attacks within the first authentication round, achieving a 100% detection and a 1.7% false alarm rate. This reflects a 16.1% improvement in detection rate and a 92.5% reduction in false alarms compared to the entanglement-based QIA benchmark.
The emergence of sixth-generation (6G) networks presents a promising paradigm aimed at meeting stringent quality-of-service (QoS) requirements, thereby paving the way for modern applications such as the metaverse. In contrast, the advent of quantum computers poses a significant threat to the security of such networks. However, the quantum Internet signifies a remarkable advancement in communication technology, harnessing the principles of quantum entanglement and superposition to facilitate unparalleled levels of security and efficient computations. Quantum communication can be achieved through the utilization of quantum entanglement. Through the exchange of entangled pairs between two entities, quantum communication becomes feasible, enabled by the process of quantum teleportation. Given the lossy nature of the channels and the exponential decoherence of the transmitted photons, a set of intermediate nodes can serve as quantum repeaters to perform entanglement swapping and directly entangle two distant nodes. Such quantum repeaters may be malicious and by setting up malicious entanglements, intermediate nodes can jeopardize the confidentiality of the quantum information exchanged between the two communication nodes. Hence, this paper proposes a quantum identity authentication protocol that protects quantum networks from malicious entanglements. Unlike the existing protocols, the proposed quantum authentication protocol does not require periodic refreshments of the shared secret keys. Simulation results demonstrate that the proposed protocol can detect malicious entanglements with a 100% probability after an average of 4 authentication rounds. Moreover, the average number of authentication rounds required to detect an attack is reduced by 69% and 76.5% compared with benchmark protocols. Index Terms—Quantum identity authentication, quantum communications, malicious entanglement, quantum security, quantum Internet, 6G.
Zero trust for wireless networks (ZTWN) is a model to ensure that all network traffic, including wireless connections, is considered untrusted and requires authentication and authorization, regardless of the network location or user identity. The infrastructure of zero trust depends on classical cryptographic techniques to guarantee the necessary security measures. Unfortunately, quantum computing has the potential to break many cryptographic techniques, which could compromise the confidentiality and integrity of data transmitted over ZTWN. Therefore, we explored the integration of various quantum components as potential solutions to enhance and achieve the security objectives of ZTWN in the long term. Furthermore, three different quantum machine learning (QML) algorithms have been investigated on two anomaly detection datasets; the achieved accuracies outperform the implemented classical. Quantum identity authentication (QIA) and quantum communication protocols leverage quantum properties, such as quantum entanglement and superposition have been developed to ensure secure and tamper-proof communication between ZTWN entities. The security analysis of the proposed protocols against inside and outside attacks proved that attackers cannot extract any secret information
The absolute security of quantum communication protocols relies on a critical premise: all participating parties are legitimate users. Ensuring the legitimacy of participant identities is paramount in complex real-world communication environments. Quantum identity authentication (QIA), in which fundamental principles of quantum mechanics are used to achieve unilateral or mutual authentication between communicating parties, constitutes an indispensable core component for building a comprehensive quantum secure communication system. It holds significant research value in the field of quantum communication. This review employs a comparative classification method to systematically outline the research trajectory of QIA protocols. By categorizing protocols based on the required quantum resources and the types of quantum protocols employed, the advantages and disadvantages of various categories are analyzed in terms of efficiency, security, and practicality. Single-photon protocols require low resources, and they are easy to implement, and compatible with existing optical components, but require high-efficiency single-photon detectors and exhibit weak noise resistance. Entangled-state protocols offer high security and strong resistance to eavesdropping, particularly suitable for long-distance or multi-party authentication. However, they greatly depend on the preparation and maintenance of high-precision, stable multi-particle entanglement sources, resulting in high experimental complexity. Continuous-variable (CV) protocols achieve high transmission efficiency in short-distance metropolitan area networks and are compatible with classical optical communication equipment, making experiments relatively straightforward. Yet, they require high-precision modulation technology and are sensitive to channel loss. Hybrid protocols aim to balance resource efficiency and security while reducing reliance on a single quantum source, but their design is complex and may introduce new attack vectors. Quantum key distribution (QKD) framework protocols embed identity authentication in the key distribution process, making them suitable for scenarios requiring long-term secure key distribution, although they often depend on pre-shared keys or trusted third parties. Quantum secure direct communication (QSDC) framework protocols integrate authentication with secure direct information transmission, offering high efficiency for real-time communication, but requiring high channel quality. Measurement-device-independent QSDC (MDI-QSDC) represents a key development direction that can resist attacks on measurement devices. Quantum teleportation (QT) framework protocols achieves cross-node authentication and unconditional security, making it suitable for quantum relay networks despite its high experimental complexity. The entanglement swapping framework protocol can resist conspiracy attacks and is suitable for multi-party joint scenarios, but it consumes a lot of resources and relies on trusted third party. Ping-pong protocol framework supports dynamic key updates and exhibits strong resistance to eavesdropping, making it suitable for temporary authentication on mobile terminals, although it typically only supports unilateral authentication and requires a bidirectional channel. Subsequently, this review details the latest QIA protocols of our research group, including a multi-party synchronous identity authentication protocol based on Greenberger-Horne-Zeilinger (GHZ) states, and a tripartite QSDC protocol with identity authentication capabilities utilizing polarization-spatial super-coding. The GHZ-based multi-party synchronous authentication protocol leverages the strong correlations inherent in GHZ states to achieve simultaneous authentication among multiple parties. Through a carefully designed two-round decoy-state detection mechanism, it effectively resists both external eavesdropping and internal attacks originating from authenticated users, thereby enhancing the efficiency and security of identity management in large-scale quantum networks. The core innovation of the polarization-spatial super-coding tripartite QSDC protocol lies in its deep integration of the authentication process with information transmission by utilizing the spatial degrees of freedom of single photons. This design accomplishes the identity verification of two senders and the transmission of secret information within a single protocol run, ensuring end-to-end security through a three-stage security check. This “authentication-as-communication” paradigm significantly improves the overall efficiency and practicality of the protocol. Its successful implementation also relies on advancements in quantum memory technology. Finally, the review outlines future research directions for quantum identity authentication and explores its potential applications in quantum communication. The QIA research needs to focus on reducing resource dependency, exploring more efficient protocol designs, further enhancing protocol integration and robustness, prioritizing the development of protocols adaptable to real-world environments, and actively investigating integration with novel scenarios. This comprehensive review aims to provide theoretical research foundations and technical support for the practical development of future quantum identity authentication.
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.
Quantum cryptography provides a feasible approach to achieving information-theoretic security in point-to-point communication. In contrast to point-to-point protocols, end-to-end secure quantum communication is particularly promising for large-scale quantum networks. Here, we propose a scheme for end-to-end secure communication with private message authentication. For practical purposes, we demonstrate the scheme in a quantum communication network with optical fibers or free-space links that integrate secure repeaters. Furthermore, we present a route proposal model for routing private messages in a quantum network based on the scheme. As messages are routed through a network, malicious nodes may tamper with the messages passing through them with some probability. Based on authentication results, we use machine learning methods to detect malicious nodes, including supervised and unsupervised algorithms. The simulation results in specific networks show that our methods can effectively detect malicious nodes under different tamper probabilities.
Abstract Quantum Key Distribution (QKD) is a cornerstone of next-generation cryptographic technologies, offering unparalleled security by utilizing the principles of quantum mechanics. However, practical implementations of QKD face significant challenges, including inefficiencies in key generation, susceptibility to noise, and vulnerabilities to eavesdropping. This work addresses these challenges by proposing a machine learning (ML)-driven approach to optimize QKD protocols, focusing on enhancing both efficiency and security. In this work it is intended to evaluate three advanced protocols Adaptive, Entanglement-Based, and Hybrid under varying conditions such as key length, noise levels, and channel stability. The ML models will include ensemble learning models namely Random Forest and XGBoost, the models will analyze the impact of key parameters on protocol performance and predict efficiency with high accuracy. The results reveal that the Adaptive protocol significantly outperforms the Entanglement-Based and Hybrid protocols, achieving an efficiency of up to 0.80 for a key length of 2500 bits, compared to 0.24 for the Entanglement-Based protocol and 0.26 for the Hybrid protocol. The Adaptive protocol also demonstrates superior detection probability (ranging from 0.84 to 0.85) and a higher probability of detecting eavesdropping (ranging from 0.43 to 0.45). Furthermore, correlation analysis shows a strong positive relationship (r = 0.9985) between key length and efficiency for the Adaptive protocol, highlighting its scalability. In contrast, the Entanglement-Based protocol shows consistent but lower performance across all metrics, while the Hybrid protocol offers a compromise but remains less efficient than the Adaptive protocol. By integrating ML with quantum cryptography, this work bridges the gap between theoretical advancements and practical implementations, providing a scalable framework for optimizing QKD systems in real-world applications. The findings underscore the importance of protocol design and optimization in achieving secure and efficient quantum communication, paving the way for the quantum internet of the future.