A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. The research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced AI-driven pattern recognition. The findings demonstrate that integrating quantum and AI-driven methods to build a more robust wireless communication infrastructure is feasible. The study concludes by highlighting the potential of hybrid quantum-AI systems as a critical first step in protecting next-generation networks.
A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. The research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced AI-driven pattern recognition. The findings demonstrate that integrating quantum and AI-driven methods to build a more robust wireless communication infrastructure is feasible. The study concludes by highlighting the potential of hybrid quantum-AI systems as a critical first step in protecting next-generation networks.
Quantum computing represents a major threat to future communication systems security: Once a certain level of maturity is reached, the security enjoyed by classical cryptographic schemes will immediately be put in check by (i) highly efficient quantum algorithms able to solve hard problems non-negligibly faster than the best-known classical solutions and (ii) potentially scalable fabrication techniques for quantum circuits. This work investigates the design of quantum-safe security primitives suitable for new cybersecurity threats in next-generation communications networks by means of the integrated use of quantum cryptography and deep learning. By combining the unconditionally secure guarantees offered by quantum cryptographic protocols with the adaptive, data-driven intelligence of deep learning models, this work proposes a holistic solution to secure communication infrastructures against not only current but also future cyber threats. A central element is the utilization of QKD-protocols that use the properties of quantum mechanics in order to securely distribute keys that are resistant to computational attacks. In parallel thereof, deep learning techniques are applied to monitor the system by analyzing real-time network traffic, identifying abnormal activities, and forecasting sophisticated attack patterns that frequently are overlooked by conventional security systems. This approach mitigates constraints of standalone quantum cryptography, including noise and hardware imperfections, and improves threat identification over traditional signature-based systems. The work studies the synergy between quantum cryptography and AI-assisted analytics, optimizing quantum channel estimation, error correction, and intrusion detection with deep learning. We conduct extensive simulations to show the efficacy of the framework against various adversarial attacks such as man in the middle, side-channel, and adversarial machine learning threats. The system has high stability without loss of quantum bit error rate, and high detection accuracy, for handling the change of attack vectors in distributed, heterogeneous communication networks.
Generation of secure communication was getting known as a promising technology called as a quantum cryptography. Unfortunately, it still faces several challenges, most notably in terms of high computational demands, scalability limitations, quantum decoherence, and vulnerability to side channel attacks, such that deployment of it in real world remains impossible yet. This study shows how Machine Learning (ML) and Deep Learning (DL) can be applied to tune the mentioned obstacles so that quantum cryptographic frameworks become more secure, more efficient and more scalable. In particular, we introduce a hybrid AI enabled model where RL can be utilized for tuning the performance of the post quantum cryptographic algorithm implementations, GANs can be adopted for measuring the robustness of the system, and FL can be used to make the quantum key distribution scalable. Besides, this thesis uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to incorporate the quantum authentication and key exchange methods. Additionally, our methodology employs techniques based on Graph Neural Networks (GNNs) to achieve the best performance in the networks and Adversarial Machine Learning (AML) to counter and detect, and then reduce cyber threats in the run time. For preferable cryptographic computations, we introduce Quantum Neural Networks (QNNs) to reduce its dependency on expensive quantum hardware. Experimental results also show that ML/DL based quantum security frameworks provide lowering of compute burden, improvement in real time security of data, and improve resistance to cyber threats. The result of this roadmap based on AI driven strategy is a comprehensive view, with a high level of direction describing the main steps to lead to post quantum security and take benefit of quantum cryptography and apply it in a multitude of significant utilization including secure communication, cryptographic financial systems or national infrastructure protection.
Abstract The emerging era of quantum computing has highlighted the importance of secure communication systems that combine quantum-resistant cryptography, quantum communication, and advanced security analytics. This systematic review critically examines hybrid models of quantum and classical artificial intelligence, focusing on architectures for quantum key distribution (QKD), intrusion detection, network management, and the integration of post-quantum cryptography. Following PRISMA 2020 guidelines, studies from January 2020 to July 2026 were sourced from IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, Wiley Online Library, and backward citation searches. Out of these, 30 primary studies met the inclusion criteria and were evaluated using an eight-item quality rubric and a five-level evidence-maturity framework. The findings are grouped into three categories: AI-enhanced QKD and secure communication, hybrid quantum–classical learning for intrusion detection, and post-quantum or hybrid cryptographic solutions. AI-supported QKD research has shown notable reductions in parameter search time while maintaining near-optimal secret-key rates, primarily in simulation settings. Hybrid quantum machine learning models showed competitive intrusion detection accuracy and F1 scores, but improvements over classical methods were modest, dataset-dependent, and often lacked comprehensive reporting on false positives, statistical significance, or computational costs. Post-quantum cryptography approaches demonstrated greater maturity, with evaluations involving Transport Layer Security (TLS), Internet Protocol Security (IPsec), embedded devices, wireless links, and hardware accelerators, although performance varied with platform resources, cryptographic object sizes, network conditions, and side-channel protections. Among the studies, half were simulation-based, 30% involved laboratory experiments, and 20% were functional prototypes; none showed sustained operational deployment of an integrated hybrid quantum–classical AI security system. The review concludes that current evidence supports application-specific feasibility rather than universal quantum advantage. Achieving practical deployment requires standardized benchmarking, comparable classical baselines, transparent cost analysis, hardware validation, interoperable protocols, and longer-term field testing.
Next generation communication networks require advanced solutions in security to prevent the complex increasing threats in cyber space. This research combines quantum cryptography and artificial intelligence (AI) as a way to make network security stronger by protecting data’s confidentiality, integrity and authentication. Four key algorithms – Quantum Key Distribution (QKD), AI based Intrusion Detection, Blockchain enhanced Encryption and Post Quantum Cryptography are studied in the paper to assess their capability to secure the communication infrastructures.
As sixth-generation (6G) wireless networks come into existence, there is more ever-growing demand for high-speed and secure communication than ever before. Conventional cryptographic methods are facing growing constraints to keep pace with the challenging reality of cyberattacks. Quantum-based communication technologies like quantum key distribution (QKD), quantum encryption, and quantum-resistant protocols provide an inherently secure means of transmitting data. This study examines how quantum communication technologies can be embedded within 6G architectures to provide enhanced data protection. With the help of AI and machine learning, 6G networks are able to anticipate threats and counter them, and quantum technologies offer unbreakable physical-layer encryption. Proposed work examines the security, architectural requirements, and implementation practicalities of this integration. In addition, we discussed the potential effect of quantum computing on present cryptographic techniques and describe future research directions for deployment at scale of quantum-secure communication in next-generation wireless systems.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.