The emergence of quantum computing poses fundamental challenges to the security assumptions underlying modern cryptographic systems, particularly Fully Homomorphic Encryption (FHE) schemes that enable computation on encrypted data. While Grover's algorithm provides a theoretical framework for quantum attacks on symmetric cryptographic primitives, its practical application to complex parameter spaces like those in CKKS FHE has remained limited.This paper presents a simulation-based, exploratory hybrid framework that combines deep neural networks with a simplified quantum search model to evaluate the post-quantum security of CKKS bootstrapping parameters under idealised conditions. The AI-enhanced system learns to identify potentially vulnerable parameter configurations through pattern recognition, then uses this knowledge to optimize the quantum oracle construction in a 4-qubit Grover's algorithm simulator. Experiments conducted on 5,000 synthetically generated parameter sets, with evaluation on 100 boundary-region configurations, demonstrate that this hybrid approach achieves a 73.4\% success rate in identifying insecure parameters under the experimental setup, representing a 30.6\% improvement over standard quantum search in the same simulated environment.Within the experimental model, this analysis indicates that under the experimental model, parameter sets nominally targeting 128-bit quantum security may exhibit effective security levels of only 86–101 bits when subjected to AI-guided search, suggesting that current FHE parameter margins warrant further investigation as quantum capabilities mature. The findings are simulation-based and should not be directly extrapolated to real-world deployments without further validation; however, they indicate that security margins may need to be increased by approximately 2.3 times to maintain true 128-bit quantum resistance against intelligent adversaries. These results have implications for post-quantum cryptographic standards and motivate further study at realistic qubit scales.
The emergence of quantum computing poses fundamental challenges to the security assumptions underlying modern cryptographic systems, particularly Fully Homomorphic Encryption (FHE) schemes that enable computation on encrypted data. While Grover's algorithm provides a theoretical framework for quantum attacks on symmetric cryptographic primitives, its practical application to complex parameter spaces like those in CKKS FHE has remained limited.This paper presents a simulation-based, exploratory hybrid framework that combines deep neural networks with a simplified quantum search model to evaluate the post-quantum security of CKKS bootstrapping parameters under idealised conditions. The AI-enhanced system learns to identify potentially vulnerable parameter configurations through pattern recognition, then uses this knowledge to optimize the quantum oracle construction in a 4-qubit Grover's algorithm simulator. Experiments conducted on 5,000 synthetically generated parameter sets, with evaluation on 100 boundary-region configurations, demonstrate that this hybrid approach achieves a 73.4% success rate in identifying insecure parameters under the experimental setup, representing a 30.6% improvement over standard quantum search in the same simulated environment.Within the experimental model, this analysis indicates that under the experimental model, parameter sets nominally targeting 128-bit quantum security may exhibit effective security levels of only 86–101 bits when subjected to AI-guided search, suggesting that current FHE parameter margins warrant further investigation as quantum capabilities mature. The findings are simulation-based and should not be directly extrapolated to real-world deployments without further validation; however, they indicate that security margins may need to be increased by approximately 2.3 times to maintain true 128-bit quantum resistance against intelligent adversaries. These results have implications for post-quantum cryptographic standards and motivate further study at realistic qubit scales.
The rapid advancement of quantum computing poses a significant threat to traditional cryptographic systems that rely on computational hardness assumptions such as integer factorization and discrete logarithms. Quantum algorithms, particularly Shor's algorithm, have the potential to break widely used cryptographic standards including RSA and Elliptic Curve Cryptography. To address these emerging challenges, this research proposes a machine learning-enhanced post-quantum cryptographic framework designed to provide adaptive and resilient data security in the quantum computing era. The proposed framework integrates post-quantum cryptographic algorithms with intelligent machine learning mechanisms to improve the robustness of encryption systems against quantum-based attacks. The system incorporates three major components: a post-quantum encryption module, a machine learning–based anomaly detection module, and an adaptive key management system powered by reinforcement learning. The anomaly detection component utilizes neural network models to monitor encrypted data streams and identify abnormal patterns that may indicate potential decryption attempts. In addition, reinforcement learning dynamically adjusts cryptographic key parameters and rotation schedules to reduce the risk of key compromise. Experimental evaluation demonstrates that the proposed framework significantly improves detection accuracy, reduces key management latency, and enhances resilience against simulated quantum decryption attacks when compared with conventional cryptographic systems. The integration of machine learning enables the framework to dynamically adapt to evolving threats while maintaining strong encryption standards. The results highlight the potential of combining artificial intelligence with post-quantum cryptography to build adaptive, intelligent, and future-proof security systems capable of protecting sensitive data in the presence of powerful quantum computing technologies.\\n\\n
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.
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 modern computing enterprises rely more on hybrid and -cloud environments, but their reliance on classical cryptography makes the environment vulnerable to quantum threats. The common RSA/ECC are now easily compromised using Shor’s algorithm while Grover’s algorithm reduces the effectiveness of AES security to almost half, making the distributed systems susceptible to potential security attacks. This paper brings out the recent trends in research on multi-cloud security strategies, the impact of quantum algorithms, the deployment framework of post quantum cryptography (PQC), Quantum key Distribution (QKD), simulation toolkits for quantum cloud environment. We proposed a Hybrid Post-Quantum Encryption Framework (HyPQEF), a layered quantum-safe framework by integrating PQC and Fully Homomorphic Encryption (FHE) with Zero-Trust architecture. The proposed work focused on the interoperability of the framework in the distributed cloud environment, performance trade-offs, by offering security hybrid cloud infrastructure against cryptographically relevant quantum threats.
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.
The advent of quantum computing poses substantial risks to conventional cryptographic mechanisms, particularly hash-based authentication and zero-knowledge identification (ZKI) protocols, which are susceptible to quantum algorithms such as Grover's and Shor's. This study presents a comprehensive benchmarking framework for evaluating the quantum resistance of cryptographic hash functions through performance metrics (execution time, memory utilization), statistical characteristics (entropy, bit-level randomness, avalanche effect), and security attributes (collision and preimage resistance) across diverse input sizes and edge conditions. A novel hybrid hashing strategy, integrating SHA-512 and BLAKE3 in a defense-in-depth configuration, is introduced to enhance post-quantum resilience. Its efficacy is validated via Grover's algorithm simulations, demonstrating a methodology for evaluating the increased computational workload for quantum search algorithms relative to conventional hash functions. The framework incorporates visualization utilities and structured reporting modules, enabling systematic assessment and practical implementation of quantum-resistant cryptographic solutions within ZKI systems. Findings indicate that while classical hashes such as SHA-256 and SHA-512 exhibit theoretically diminished security in quantum threat scenarios, the proposed hybrid method acts as a practical risk mitigation strategy with acceptable computational overhead, providing a viable pathway for safeguarding authentication systems against quantum-capable adversaries.