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.
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.
The advent of quantum computing presents a potential threat to conventional cryptographic systems, prompting the urgent need for post-quantum cryptography (PQC) solutions. However, the complexity of these cryptographic protocols often leads to inefficiencies in performance, hindering their widespread adoption. This paper explores the integration of machine learning (ML) techniques to optimize the performance of post-quantum cryptographic protocols. We propose an innovative framework where ML algorithms are leveraged to enhance the efficiency of key generation, encryption, decryption, and signature processes in PQC schemes. By utilizing data-driven approaches such as reinforcement learning and neural networks, we aim to reduce computational overhead and improve latency, while maintaining the robustness and security of post-quantum algorithms. Experimental results demonstrate significant performance improvements over traditional optimization methods, showcasing the potential of ML in advancing the practicality of PQC protocols. This work lays the foundation for a new class of cryptographic systems capable of withstanding quantum threats while maintaining high operational efficiency.
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.
Classical cryptographic have a threat because of the emergence of quantum computing, and hence there is an urgent need to research post-quantum cryptography. To evaluate the potential of post-quantum encryption algorithms in countering quantum threats, here the study sees how they can be integrated with artificial intelligence (AI) and machine learning (ML). For their theoretical quantum resistance, the study looks at established post-quantum cryptography methods such as lattice-based, hash-based, and multivariate cryptographic schemes. A review of current research also simulation-based research is also taken into account to review the potential of AI/ML in bolstering security protocols and simplifying encryption processes. The key takeaway is that machine learning and AI do have actual potential to enhance post-quantum encryption through increasing its efficiency, flexibility, and resistance to future quantum attacks. AI also assists in real-time responsiveness in avoiding possible vulnerabilities. Our work contributes to the growing literature by combining insights from cryptographic and AI/ML research, providing a comprehensive evaluation of post-quantum encryption methods. This study emphasizes the highest importance of adopting hybrid cryptography techniques in order to advance information security in the quantum era, leading to further research in practical applications.
The looming threat of quantum attacks on the digital infrastructure protected by conventional cryptographic protocol has generated urgency in identifying and deploying countermeasures that can mitigate the threat. There is a need for stronger cryptographic schemes that combine the strengths of both classic and quantum technologies. Post-Quantum Cryptography (PQC) has emerged as a potential solution that can withstand the challenges posed by advances in quantum computing. Owing to the increasing importance of PQC, the present research is an attempt to assess the existing research done so far so that the existing gaps can be identified which can then strengthen the existing literature. The systematic literature review presented in this work has outlined six key categories of PQC, namely, lattice-based, code-based, hash-based, multivariate, isogeny-based, symmetric-key-based cryptosystems. The study concluded that the advances made in quantum computing will result in the development of quantum computers with superior computational power. Such highly efficient quantum computers will have the ability to break the currently available cryptography schemes most used in a variety of practical applications. With the advent of quantum computing, the computational capabilities of the potential cyber attackers would grow exponentially, which would render the traditional cyber security measures inadequate. This calls for techniques like PQC to be developed and strengthen.
This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion. Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns. A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience. The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.