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.
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.
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.
This study explores how to build quantum-resistant systems to safeguard digital infrastructure in the post-quantum era by uncovering the innovations, applications, algorithms, and challenges of Quantum Computing cryptography. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach a search was conducted across the following databases for the years 2021–2025: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. We shortlisted 15 studies from 519 screened articles for a comprehensive evaluation based on their relevance. Findings show strong adoption in finance, healthcare, IoT, cybersecurity, and e-government, with lattice-based PQC emerging as the most dominant cryptographic family, followed by QKD and hybrid PQC–QKD models. The review highlights key obstacles, including transition complexity, lack of global standards, high implementation costs, and integration difficulty. The study contributes by providing the first sector-aligned synthesis of innovations, identifying algorithmic trends, and mapping global research disparities through a conceptual model. It also presents a structured set of future research directions to guide policymakers, cryptographers, and practitioners preparing for quantum-enabled threats.
The convergence of quantum computing and artificial intelligence (AI) introduces both opportunities and risks for modern cryptographic systems. This review synthesizes peer-reviewed work from 2010 to 2025 using a PRISMA-inspired methodology to examine Quantum Artificial Intelligence (QAI) as a dual-use paradigm in cryptographic security. Conceptually, the study proposes a framework that characterizes QAI both as a defensive enabler and a potential attack amplifier across key distribution, randomness generation, and intrusion detection, and it analyzes how QAI reshapes the traditional separation between post-quantum cryptography (PQC) and quantum key distribution (QKD) by adding an adaptive “learning layer” on top of classical and quantum protocols. Analytically, the review shows that QAI-enhanced schemes can improve key rates, entropy verification, and detection accuracy in realistic settings, while also enabling quantum-accelerated cryptanalysis and AI-optimized side-channel attacks. Representative case studies, including satellite-based QKD demonstrations, NIST PQC standardization efforts, QAI-based intrusion detection in 5G/6G networks, and blockchain-supported fraud detection, are used to ground these insights. The paper argues that QAI is expected to play an increasingly important role in shaping the future foundations of cryptographic trust in the quantum era. This paper does not report new experimental or deployment results; instead, it provides a PRISMA-inspired systematic review and a conceptual multi-layered security framework, together with a research agenda for QAI-enhanced cryptographic security.
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.
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.
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.