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 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 evolution of quantum computing poses a significant challenge to traditional encryption systems, with the potential to compromise the security of sensitive digital infrastructures. Post-Quantum Cryptography (PQC) has emerged as a vital field, aiming to develop encryption algorithms resilient to quantum attacks. Simultaneously, Quantum Machine Learning (QML) is revolutionizing the way machine learning models process data, offering new avenues for enhancing cybersecurity measures. This chapter explores the integration of PQC and QML to create robust, future-proof encryption systems capable of adapting to the evolving threat landscape. By examining hybrid models that combine the quantum resistance of PQC with the adaptability and efficiency of QML, this work highlights the potential for creating scalable and efficient cryptographic frameworks. The challenges and opportunities presented by the intersection of PQC and QML are discussed, with a focus on resource-constrained environments where computational power and memory are limited. Through this analysis, the chapter offers a comprehensive roadmap for advancing AI-driven, quantum-resistant cybersecurity solutions, addressing both theoretical advancements and practical implementation challenges.
Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.
Abstract : Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.
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.
This research paper provides a comprehensive analysis of how the convergence of Artificial Intelligence (AI) and Quantum Computing is reshaping modern cybersecurity. It examines emerging threats such as AI-driven attacks, deepfakes, automated vulnerability discovery, adversarial machine learning, and the future impact of quantum computers on classical cryptographic systems. The study evaluates NIST-standardized post-quantum cryptography algorithms, including CRYSTALS-Kyber, Dilithium, FALCON, and SPHINCS+, comparing their performance, implementation challenges, and readiness for large-scale deployment. It also investigates AI-based threat detection models, organizational preparedness across sectors, and hybrid security architectures that combine classical and quantum-safe methods. The paper highlights urgency due to accelerating quantum hardware advancements and recommends strategic steps for organizations, vendors, policymakers, and researchers to transition toward quantum-resilient cybersecurity.
The incoming need for defense against quantum computer attacks has motivated researchers to prioritize post-quantum cryptography (PQC) because this approach develops encryption which quantum computers cannot break. A great number of PQC algorithms create complex challenges regarding both computational requirements and key length as well as potential security weaknesses that require optimization for actual implementation. ML technology provides an approach to enhance the performance of post-quantum cryptography systems while securing their operations and making them more adaptable. The key generation process becomes more efficient while parameter selection reaches optimized results thanks to ML techniques which strengthen attack resilience through implementation protection against anomalies and side-channel attacks. The paper explores the ML and PQC connection while presenting potential ML applications that enhance the effectiveness of PQC methodologies including lattice-based schemes hash-based schemes and multivariate-quadratic and code-based approaches. The research document outlines integration problems and prospective directions between PQC and ML which incorporate considerations for security attacks and operational and computational constraints. Through their union PQC and ML enable the creation of additional secure cryptographic technologies that provide efficient scaling potential to overcome quantum computing threats.