Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device participation, non-convex loss functions, and heterogeneous data distributions, (ii) developing a novel multi-layered post-quantum cryptographic architecture with a quantum neural network-powered adaptive mechanism that monitors conditions, evaluates threats, and adjusts parameters across three National Institute of Standards and Technology (NIST)-compliant levels. Our theoretical framework and empirical validation reveal two key insights: (i) the derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset; and (ii) findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security, while confirming that our adaptive framework effectively mitigates these overheads to preserve overall system efficiency. Specifically, the hardware experiments demonstrate that our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%. Furthermore, extensive simulations validate our theoretical analysis, showing strong agreement between predicted and observed convergence trends. The coupling of these two stages ensures both theoretically grounded performance optimization and adaptive threat resilience, enabling efficient, secure, and scalable DQL deployment.
Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device participation, non-convex loss functions, and heterogeneous data distributions, (ii) developing a novel multi-layered post-quantum cryptographic architecture with a quantum neural network-powered adaptive mechanism that monitors conditions, evaluates threats, and adjusts parameters across three National Institute of Standards and Technology (NIST)-compliant levels. Our theoretical framework and empirical validation reveal two key insights: (i) the derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset; and (ii) findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security, while confirming that our adaptive framework effectively mitigates these overheads to preserve overall system efficiency. Specifically, the hardware experiments demonstrate that our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%. Furthermore, extensive simulations validate our theoretical analysis, showing strong agreement between predicted and observed convergence trends. The coupling of these two stages ensures both theoretically grounded performance optimization and adaptive threat resilience, enabling efficient, secure, and scalable DQL deployment.
Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device participation, non-convex loss functions, and heterogeneous data distributions, (ii) developing a novel multi-layered post-quantum cryptographic architecture with a quantum neural network-powered adaptive mechanism that monitors conditions, evaluates threats, and adjusts parameters across three National Institute of Standards and Technology (NIST)-compliant levels. Our theoretical framework and empirical validation reveal two key insights: (i) the derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset; and (ii) findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security, while confirming that our adaptive framework effectively mitigates these overheads to preserve overall system efficiency. Specifically, the hardware experiments demonstrate that our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%. Furthermore, extensive simulations validate our theoretical analysis.....
The rapid advancement of quantum computing presents a major threat to conventional public-key cryptographic systems, thereby accelerating the global adoption of Post-Quantum Cryptography (PQC) standards approved by the National Institute of Standards and Technology (NIST). Although PQC algorithms are specifically designed to resist quantum-based attacks, they remain vulnerable to implementation-level weaknesses such as side-channel leakage, fault injection attacks, and machine-learning-assisted cryptanalysis. This research investigates the emerging role of Quantum Machine Learning (QML) in strengthening and evaluating the security of PQC systems. The study explores the dual application of QML as both an offensive and defensive cybersecurity mechanism. On the offensive side, quantum-enhanced learning models, including variational quantum classifiers and quantum kernel-based approaches, are analysed for their capability to accelerate side-channel analysis and cryptanalytic attacks by learning hidden leakage patterns from power consumption traces, electromagnetic emissions, and timing information more efficiently than conventional machine learning models. On the defensive side, the research proposes QML-driven countermeasures such as quantum generative models for creating randomized noise distributions that obscure implementation leakage and reinforcement learning agents capable of dynamically adapting cryptographic masking techniques in real time. Experimental implementation will be conducted using IBM Quantum hardware alongside PQC algorithms including CRYSTALS-Kyber and CRYSTALS-Dilithium. The research will evaluate the comparative performance of quantum-assisted attack and defence mechanisms with respect to detection efficiency, leakage resilience, and computational overhead. Expected outcomes include the development of open-source QML-based cybersecurity toolkits, comprehensive security evaluation frameworks for PQC migration strategies, and the establishment of a “quantum-secure-by-design” approach for intelligent cryptographic systems. The study further supports global efforts toward quantum-safe infrastructure development and promotes collaborative cybersecurity research initiatives between the United States and Nigeria.
The rapid advancement of quantum computing presents both unprecedented opportunities and significant threats to modern cybersecurity. As quantum processors progress toward practical large-scale deployment, widely used cryptographic systems, particularly RSA, ECC, and other public-key mechanisms, face the risk of becoming obsolete. This review examines how quantum technologies can be strategically leveraged to enhance cybersecurity while simultaneously addressing the vulnerabilities introduced by quantum-enabled attacks. A systematic analysis of 35-40 peer-reviewed studies was conducted to evaluate developments in post-quantum cryptography, quantum-resistant security protocols, Quantum Key Distribution (QKD), and hybrid classical–quantum defense architectures. The findings reveal a rapidly evolving research landscape focused on resilient lattice-based cryptography, scalable quantum-safe communication frameworks, and early-stage quantum machine learning applications for intrusion detection. The review highlights both the promise and current limitations of quantum-driven security solutions, including hardware constraints, integration challenges, and the need for global standardization. The study underscores the urgency for governments, industries, and researchers to adopt quantum-ready cybersecurity strategies and accelerate the transition to quantum-resilient infrastructures.
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
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.
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.