The many forms of wireless communication used these days have created a greater demand for strong and flexible cryptographic methods to protect from new security risks that can happen as quantum computing advances.Diffie-Hellman and RSA approaches have been basic to cryptography, but work on these can now be weakened by attack methods such as Shor's and Grover's algorithms following quantum developments which undermines many post-quantum schemes.In our research, we develop a plan using Quantum Machine Learning (QML) to help ensure the security of future wireless networks.As a result, this design pairs quantum theory with reinforcement learning to create strong keys that cannot be accessed by attackers.Value is drawn from quantum entanglement and random measurements of qubits to create key materials needed for physically unclonable keys and at the same time, reinforcement agents support optimizing agreement policies based on variations detected in the channel.Simulations were run using Rayleigh fading and multi-SNR using Qiskit and TensorFlow.The system achieved entropy greater than 95%, agreed on important issues more than 98% successfully and brought latency down to just 0.13 seconds, outshining both classical and post-quantum cryptography in performance, flexibility and safety from attacks.Moreover, the presented encryption solution managed to resist hackers, even if the environment was noisy.As a quantum-AI combination, it is well-positioned to serve as a platform to safely communicate over wireless networks.It builds the basis for examining embedded QML hardware versions of IoT, vehicle networks and smart grid communications, since fast security and reliable performance without high delay are required.
The many forms of wireless communication used these days have created a greater demand for strong and flexible cryptographic methods to protect from new security risks that can happen as quantum computing advances.Diffie-Hellman and RSA approaches have been basic to cryptography, but work on these can now be weakened by attack methods such as Shor's and Grover's algorithms following quantum developments which undermines many post-quantum schemes.In our research, we develop a plan using Quantum Machine Learning (QML) to help ensure the security of future wireless networks.As a result, this design pairs quantum theory with reinforcement learning to create strong keys that cannot be accessed by attackers.Value is drawn from quantum entanglement and random measurements of qubits to create key materials needed for physically unclonable keys and at the same time, reinforcement agents support optimizing agreement policies based on variations detected in the channel.Simulations were run using Rayleigh fading and multi-SNR using Qiskit and TensorFlow.The system achieved entropy greater than 95%, agreed on important issues more than 98% successfully and brought latency down to just 0.13 seconds, outshining both classical and post-quantum cryptography in performance, flexibility and safety from attacks.Moreover, the presented encryption solution managed to resist hackers, even if the environment was noisy.As a quantum-AI combination, it is well-positioned to serve as a platform to safely communicate over wireless networks.It builds the basis for examining embedded QML hardware versions of IoT, vehicle networks and smart grid communications, since fast security and reliable performance without high delay are required.
Cryptography enables modern-day secure communication. The advancement of quantum algorithms has made classical cryptosystems ineffective. Important data has been encrypted and verified using digital signatures and cryptography. Classical cryptography systems were on the edge of collapse when quantum algorithms were introduced, since quantum computers can solve complex problems in less time. It is important to be prepared for future threats, even if standard cryptosystems are presumably safe for the moment being because quantum computing is not yet developed. One use of quantum cryptography is quantum key distribution (QKD), which is a way to safely share encryption keys between communication parties by utilizing quantum physics. Hybrid classical-quantum methods offer protection from both classical & quantum attackers, which can help smooth the way for the change. We introduce a new way to build hybrid encryption schemes from preexisting ones. First, the paper examines classical cryptosystems vulnerability to quantum computers. Then, it investigate into different families of post-quantum cryptosystems, checks in on the NIST's post-quantum cryptography consistency process, and finally, compares the performance of algorithms using post-quantum cryptography on various platforms. Along with discussing and evaluating current methods, this paper also discusses a novel hybrid quantum cryptographic system that employs message authentication codes with enhanced lattice based cryptography.
The rapid advancement of quantum computing threatens the foundations of classical cryptography, creating severe risks for resource-constrained IoT systems widely used in healthcare, autonomous vehicles, and industrial automation. Conventional algorithms like RSA and ECC are vulnerable to Shor's and Grover's algorithms, exposing IoT to passive and active quantum-based attacks. This manuscript proposes a quantum-safe framework integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Artificial Intelligence (AI) to ensure confidentiality, integrity, and resilience. The framework leverages lattice, hash, and code-based PQC, lightweight cryptographic protocols, hybrid security models, and efficient key management to protect IoT communications. By combining PQC, QKD, and AI-driven adaptive security, the framework offers scalable, sustainable, and future-proof protection in the post-quantum era.
The rapid advancement of quantum computing poses a significant threat to traditional cryptographic systems that rely on computational hardness assumptions such as integer factorization and discrete logarithms. Quantum algorithms, particularly Shor's algorithm, have the potential to break widely used cryptographic standards including RSA and Elliptic Curve Cryptography. To address these emerging challenges, this research proposes a machine learning-enhanced post-quantum cryptographic framework designed to provide adaptive and resilient data security in the quantum computing era. The proposed framework integrates post-quantum cryptographic algorithms with intelligent machine learning mechanisms to improve the robustness of encryption systems against quantum-based attacks. The system incorporates three major components: a post-quantum encryption module, a machine learning–based anomaly detection module, and an adaptive key management system powered by reinforcement learning. The anomaly detection component utilizes neural network models to monitor encrypted data streams and identify abnormal patterns that may indicate potential decryption attempts. In addition, reinforcement learning dynamically adjusts cryptographic key parameters and rotation schedules to reduce the risk of key compromise. Experimental evaluation demonstrates that the proposed framework significantly improves detection accuracy, reduces key management latency, and enhances resilience against simulated quantum decryption attacks when compared with conventional cryptographic systems. The integration of machine learning enables the framework to dynamically adapt to evolving threats while maintaining strong encryption standards. The results highlight the potential of combining artificial intelligence with post-quantum cryptography to build adaptive, intelligent, and future-proof security systems capable of protecting sensitive data in the presence of powerful quantum computing technologies.\\n\\n
Quantum computing is on the cusp of transforming the computational strength of “Intelligent Transportation Systems” (ITSs), but issues such as the scalability of qubits and error correction continue. With wireless networks shifting from 5G to 6G, technologies such as extended mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and the Internet of Things (IoT) will transform global connectivity. Though 5G offers improved speeds, it is prone to threats based on quantum computing, making quantum-safe cryptographic solutions for 6G networks essential. This paper discusses the revolutionary capability of quantum machine learning (QML) to meet the changing needs of future wireless systems. Through the use of quantum algorithms for data processing, QML can improve network performance, real-time decision-making, and increase security with quantum-safe cryptography. This paper sets out the challenges and prospects for the integration of quantum technologies into the design and deployment of secure, efficient, and scalable 6G wireless communication systems.
The Internet of Things (IoT) now underpins critical systems like healthcare monitors, smart home, smart grids, automotive V2V & V2X networks, and industrial controls but it still relies on classical cryptography, whose security assumptions are vulnerable to quantum computers. A large quantum computer could run Shor’s algorithm to break RSA/ECC and use Grover’s algorithm to accelerate brute-forcing of symmetric keys, undermining the confidentiality and authentication of data in these IoT applications. Researchers have proposed quantum cryptographic solutions. Quantum key distribution (QKD) and quantum random number generators (QRNGs) exploit quantum mechanics to detect eavesdropping and deliver provably secure key material. For example, chip-scale QRNGs can be embedded in IoT sensors to generate true random keys, boosting resistance to brute-force attacks. These quantum techniques provide information-theoretic secrecy, for instance, QKD-generated keys have been used to authenticate smart-grid SCADA. However, most IoT devices are resource-constrained, making direct deployment of conventional QKD hardware challenging. Post-quantum cryptography (PQC) complements quantum methods by providing algorithms such as lattice-based, hash-based that resist quantum attacks on existing hardware. Standardized PQC can be deployed via software updates on legacy IoT nodes, enabling scalable and quantum-safe security within current constraints. Researchers are exploring lightweight PQC for connected cars and e-health sensors. Hybrid approaches combining QKD/QRNG with PQC offer a layered defense. Since QKD protocols are regarded as ‘future-proof’ and PQC standards mature, their combined use will be essential for robust, quantum-safe IoT security. Miniaturizing quantum components such as integrated QKD/QRNG modules and optimizing lightweight PQC schemes for low-power IoT devices are future research directions to secure IoT networks.
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.