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.
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.
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.
The integration of Quantum Key Distribution (QKD) with Machine Learning (ML) has developed a high-end, secure model for accurate data transmission and network security. Many existing systems face several issues due to advances in quantum computing. Thus, integrating these models improved the efficiency, accuracy, and feasibility of QKD systems. In this paper, an Advanced Learning Approach (ALA) is combined with the Quantum Key Distribution Framework to refine key generation, error correction, and intrusion detection in quantum communication channels. The proposed ALA also used reinforcement learning algorithms to predict quantum channel fluctuations, dynamically adjust key exchange parameters, and strengthen authentication protocols. Simulation results demonstrate improved key rates, reduced Quantum Bit Error Rates (QBER), and enhanced resilience against eavesdropping attacks. The study contributes to the realization of practical, scalable, and intelligent quantum-secure communication systems—bridging the gap between theoretical quantum security and real-world network deployment.
In the wireless communication network, transmitting messages from sender to receiver may traverse several intermediate nodes. Any eavesdropper can attack in the communication channel, and any malicious intermediate node can act as receiver to intercept the message. The secure routing path must solve channel attacks and attacks in the malicious node. In the classical field, the solution of the channel attacks is conditional security and we can not provide secure communication in the routing path from sender to receiver. In the quantum field, the security of the authentication protocol is based on the laws of nature, including no-cloning theorem and quantum teleportation. According to the property of no-cloning theorem and quantum teleportation, we can use the shared quantum entangled particles between communicators for liar detection problem. In the quantum cryptography, the security of the authentication protocol is unconditional security. This paper designs the two-way secure communication protocol, which can set up the secure routing path from sender to receiver. The two-way secure communication protocol includes the sender oriented checking and the receiver oriented checking. By using quantum sharing table, these checking schemes investigate whether the routing path is secure or not. In the beginning, the sender and receiver pre-share a quantum sharing table to detect all the dishonest nodes. The quantum sharing table acts as a quantum key distribution which can not be stolen by any Eve.
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.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)