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.
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.
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.
The rapid advancement of quantum computing poses a significant threat to the confidentiality and integrity of classified and defense communications. Quantum algorithms have the potential to undermine widely deployed public key cryptographic schemes, exposing sensitive military and governmental information to interception and decryption. Foundational work in quantum cryptography has demonstrated that the principles of quantum mechanics can be leveraged to achieve fundamentally secure key distribution, offering a promising countermeasure to quantum-enabled attacks (Bennett & Brassard, 2014; Ekert, 1991). As defense communication systems increasingly rely on longterm data confidentiality, the urgency of transitioning toward quantum-resilient security architectures has become a strategic priority. Despite their widespread adoption, classical cryptographic mechanisms are increasingly vulnerable in the post-quantum era. Studies on post-quantum cryptography have shown that many conventional encryption and key exchange algorithms are susceptible to quantum adversaries, necessitating the development and standardization of new cryptographic primitives (Chen et al., 2016). Ongoing standardization efforts further highlight the complexity of migrating large-scale, mission-critical systems to quantum-safe solutions, particularly within defense environments where interoperability, reliability, and assurance requirements are stringent (Alagic et al., 2025). In parallel, artificial intelligence has emerged as a powerful enabler of advanced security analytics. Machine learning and deep learning techniques enhance intrusion detection, anomaly identification, and adaptive response capabilities within complex communication infrastructures (Buczak & Guven, 2016). Recent research also demonstrates that AI can be applied directly within quantum cryptographic systems, for example by improving signal processing, error correction, and performance optimization in quantum key distribution implementations (Chin et al., 2021). The convergence of quantum cryptography and AIdriven analytics therefore offers a complementary and proactive approach to securing defense communications against both classical and quantum threats. This paper analyzes the integration of quantum cryptography and artificial intelligence as a unified framework for protecting classified and defense communications. It synthesizes advances in quantum key distribution, post-quantum cryptography, and AI-based security analytics, and proposes an integrated perspective that enhances resilience, detection capability, and operational robustness. The study highlights key implications for defense communication systems, emphasizing how AI-enhanced quantum security architectures can support long-term confidentiality, adaptive defense, and strategic cyber resilience in the evolving threat landscape (Sarker et al., 2021).
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.
The advent of quantum computing introduces fundamental challenges to contemporary cybersecurity, particularly by undermining the security assumptions of widely deployed cryptographic schemes. In parallel, the increasing sophistication, stealth, and adaptability of modern cyber threats expose the limitations of traditional, reactive security monitoring approaches. These developments motivate a shift toward defense strategies that are both quantum-resilient and intelligence-driven. This paper presents a comprehensive review of the convergence between post-quantum cryptography (PQC) and AI-driven threat hunting, focusing on the design and evolution of quantum-resilient defense frameworks. Rather than emphasizing empirical performance comparisons or speculative quantitative claims, the review adopts a concept-centric and analytically grounded perspective. It examines the foundational principles of post-quantum cryptography, the role of artificial intelligence in proactive threat hunting, and the emerging need to integrate cryptographic assurance with adaptive, learning-based security mechanisms. Existing architectural models, trust and risk abstractions, and formal security properties are analyzed to highlight how AI techniques can complement PQC by enabling continuous threat inference, contextual awareness, and adaptive response in post-quantum environments. The paper further synthesizes current research into a unified conceptual framework that captures the interaction between quantum-resistant cryptographic primitives and AI-based threat hunting processes. Key research gaps and open challenges are identified, including issues related to scalability, explainability, validation under future threat models, and the co-evolution of cryptographic and intelligent defense mechanisms. By consolidating fragmented research directions into a coherent analytical narrative, this review aims to serve as a foundational reference for future work on integrated, quantum-resilient cybersecurity architectures.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.