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Journal of Electrical Systems and Information Technology

Hybrid quantum–classical AI models for secure communications: a systematic review

Bernard Kyiewu, Clinton Amponsah, Odoi Henry, Linda Bessa-Simons, Richard Komba

Published Aug 28, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.1186/s43067-026-00390-6

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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.
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 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.
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 evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport Layer Security (TLS) and Internet Protocol Security (IPsec) become vulnerable to quantum attacks. This paper presents a structured survey based on a systematic classification of recent literature across AI-enabled networking, Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level integration approaches. The review categorizes existing works into AI-driven network optimization, quantum-resilient security mechanisms, and hybrid AI–QRC architectural frameworks, enabling a unified comparison of design strategies and deployment models. Furthermore, this survey analyzes how Artificial Intelligence (AI) techniques enable autonomous network operations while Quantum-Resilient Cryptography (QRC) approaches provide post-quantum security guarantees, highlighting their complementary roles in next-generation network design. The study further synthesizes key findings for a tightly coupled AI–QRC co-design, which remains an open challenge due to mismatched assumptions in control latency, cryptographic overhead, and protocol adaptability. Additionally, this review emphasizes the need for integrated frameworks that combine intelligence and quantum security to develop adaptive, scalable, and resilient network protocols for the post-quantum era.
The current security systems cannot be considered long term sustainable because there is a quantum rate of development of the computing which directly and immediately threatens the classical cryptograph systems. In this respect, the research paper will undertake efforts in giving the concept of an AI-led adaptive mechanism of Quantum Key Distribution (QKD) protocols that will be self-sufficient in guarding and overseeing quantum communication networks against countermeasures towards the constantly evolving threats. The model combines quantum resources management and real-time threat analysis and optimization of dynamic parameter into one flow. Quantum channel noises can be predictedively implemented on machine learning using reinforcement learning, continued intrusion and anomaly detection on neural networks, and independent optimization of the parameters of QKD in dynamical communication systems. Post-quantum cryptography PQC algorithm-based lattice, hash-based and multivariate cryptography are also provided in the system as a backup mechanism to provide the confidence of the hybrid communication environments with the future compatibility of the classical and quantum infrastructural systems. In other cases, the framework will enhance quantum- enhanced blockchain where the AI-enhanced verification enhances the integrity of the distributed registries and resilience in the interparty communication networks. The ethical considerations and the need to have light and interoperative standards are the other considerations since it needs to be able to deploy on a large scale. Instead of calculating the performance standards, the provided research involves the conceptual architecture diagram of the way the predictive AI models can be logically aligned with the natural security features of quantum mechanics to a new generation cryptographic ecosystem that will be able to fight the classical and the quantum adversaries simultaneously.
Quantum computing advances pose a serious threat to conventional public-key cryptographic systems such as RSA and elliptic curve cryptography as quantum algorithms may undermine the computational assumptions that guaranty their security. This challenge has driven the development of quantumsafe security approaches among which Quantum Key Distribution (QKD) represents a fundamentally different paradigm based on quantum mechanical principles rather than mathematical complexity. This paper presents a structured review of recent progress in QKD based on a systematic analysis of literature between 2020 and 2025, we examine core protocols including discrete-variable, continuous-variable, measurement-deviceindependent, and twin-field approaches, while also discussing their relationship with post-quantum cryptography as a complementary strategy for future secure communication systems. We provide a unified comparison framework to support practical design and policy decisions by organizing protocols and implementations along deployment dimensions. The study analyze theoretical foundations, implementation technologies across fiber, free-space, satellite, and integrated photonic platforms, and key performance trade-offs involving scalability, security robustness, and deployment feasibility. Findings indicate substantial progress in transitioning QKD from laboratory research toward practical implementations, supported by advances in network architectures, integrated hardware and hybrid cryptographic designs. The comparative analysis will highlight distinct strengths and limitations among protocol families and emphasize the potential of combining QKD with post-quantum cryptographic methods to enhance resilience. Remaining challenges including scalability constraints, implementation-level vulnerabilities, performance limitations over long distances, and integration with existing communication infrastructures are critically discussed. Finally, emerging research directions such as quantum repeaters, hybrid quantum-classical security models, and exploratory directions such as artificial intelligence-assisted optimizations are identified as key factors in enabling the evolution of QKD toward largescale quantum-secure networking.