Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 17 references
Abstract
We present Q-OPSEC, an adaptive middleware that uses supervised, unsupervised and reinforcement learning to select cryptographic strategies from classical, post-quantum and quantum-assisted (QKD) options. The selection is modeled as a multi-objective MDP that balances security, latency, computational and energy cost, and compliance. A negotiator and registry enforce hard constraints, handle endpoint compatibility and fallbacks, and store empirical cost profiles. Experiments in simulated smart environments and hardware-in-the-loop tests show high success rates ($>95 \%$) and context-aware adaptation. Limitations include reliance on simulated QKD channels, limited device profiling, empirically tuned hyperparameters, and evaluation in high-performance environments, which may not reflect IoT constraints; future work targets real QKD integration, broader benchmarking, robust RL methods, federated learning and explainability.
Quantum Key Distribution (QKD) has turned out to be a promising approach to secure communication systems. Twin-field QKD (TF-QKD) is one of the protocols that allow long-range secure communication to be provided beyond the traditional rate-distance constraints. Nevertheless, the instability of the phases, noises in the channels, and fixed mechanisms to control the system pose challenges to implementing large-scale TF-QKD networks in practice. The present paper suggests an AI-assisted adaptive framework of TF-QKD networks to enhance the performance of robustness, scalability, and communication. The proposed solution incorporates a predictive model which is based on machine learning to estimate phase drift, optimize correction parameters in real time. The hybrid quantum-classical architecture is designed in which an intelligent control layer is continuous to monitor channel conditions and optimize system adaptability. Simulation outcomes have shown that proposed framework can reduce Quantum Bit Error Rate (QBER) of the system by about 20-30% and increase the rate of secure key generation and overall system performance when compared to traditional methods of these systems where the system is controlled by a static system. A better scalability and stability of the proposed system to different channel conditions is also observed. These findings indicate the possibilities of incorporating artificial intelligence into the future intelligent quantum communication networks.
Narmatha V, P. Reginald· 2026 7th International Confe...· 0 citations
Distributed infrastructure schedulers traditionally optimise capacity, locality, and cost, but provide limited support for security posture and emerging quantum-classical workloads. As hybrid quantum-classical computing becomes increasingly practical and post-quantum security requirements begin to affect infrastructure deployment, schedulers must jointly reason about heterogeneous compute resources, security constraints, and quantum backend characteristics. We present SQUIRO, a framework for security-aware quantum-classical scheduling based on a platform-independent Unified Scheduling Model (USM) and a six-step Scheduler Design Methodology (SDM) that together enable the derivation of concrete schedulers for Kubernetes, high-performance computing (HPC), and federated environments. The framework combines multidimensional security posture enforcement through hard feasibility constraints with residual-risk optimisation, and introduces a circuit-aware quantum backend selector that accounts for coherence margin, calibration freshness, queue pressure, and hardware capabilities through a forward-compatible colocation hierarchy. Evaluation on synthetic Kubernetes clusters shows that the security model enforces complete compliance for regulated workloads by construction, while global optimisation reduces infrastructure cost by up to 51% and energy consumption by up to 63% compared with greedy placement in underloaded scenarios, without compromising admission priorities. Additional experiments characterise the solve-time growth of the current CP-SAT formulation and show that circuit-aware backend selection systematically diverges from naive error-rate ranking under coherence- and queue-limited conditions.
Quantum Key Distribution (QKD) provides information-theoretic security grounded in the laws of quantum mechanics, yet practical deployment increasingly extends beyond conventional point-to-point fiber links. Several rapidly emerging QKD directions are often studied separately, including adaptive protocol and parameter support; free-space, satellite, UAV, and high-altitude platform (HAP) channels; integration with IoT and 6G networks; quantum-secured federated learning; Quantum Machine Learning (QML) assisted decision support; and steerability-aware estimation for one-sided device-independent QKD. This survey examines how Machine Learning (ML), Reinforcement Learning (RL), and QML address these specialized scenarios and organizes the literature into five thematic pillars: (I) adaptive protocol and parameter support; (II) free-space, satellite, UAV, and HAP-assisted QKD; (III) QKD for IoT, 6G, and quantum-secured federated learning; (IV) QML-assisted QKD functions; and (V) steerability-aware and one-sided device-independent QKD security estimation. For each theme, we follow a consistent problem, conventional solution, and ML/RL/QML solution structure and summarize reported gains using metrics such as accuracy, mean absolute percentage error, QBER reduction, and secret key rate improvement. We further provide thematic and cross-theme comparison tables and identify open challenges, including dataset scarcity, transferability across weather and mobility conditions, interpretability, trustworthy QML, and the boundary between ML-based decision support and security certification. This survey serves as a focused reference for adaptive, non-terrestrial, and application-integrated QKD systems.
Primitive quantum speedups are interface-relative: they depend on the input access used to run the primitive and on the output contract used to consume its state or samples. This paper introduces a transcript-level admissibility relation \(A_M\preceq_{\mathrm{int}}A_Q\), defined relative to the declared implementation package of the quantum interface. It identifies which adaptive classical access transcripts that same package licenses, with all setup, transcript-generation, and precision overheads charged. The main application is an operational audit for normalized-Betti estimation in clique-complex TDA, separating three declared-interface regimes. Reversible indexed simplex interfaces certify matched classical simplex sampling and local Laplacian row access by evaluating their reversible routines on single computational branches. Membership-based preparations induce a rejection route of overhead \(\binom{n}{k+1}/|S_k|\). Abstract spectral or block-encoding interfaces require an accompanying implementation package, transcript reduction, or shared representation. Under the indexed certificate and interface closure, the end-to-end cost is fixed by the imported estimator's spectral dependence on the gap \(\gamma\); the concretely realized bounded-treewidth family already admits exact \(\mathrm{poly}(n)\) classical Betti computation by rank over \(\mathbb{Q}\). A low-rank separation supports the role of access and output contracts.
Pablo Herrero Gómez, A. Morenilla, David Muñoz Hernández et al.· 0 citations
This systematic review critically examines hybrid models of quantum and classical artificial intelligence, focusing on architectures for quantum key distribution, intrusion detection, network management, and the integration of post-quantum cryptography, concluding that current evidence supports application-specific feasibility rather than universal quantum advantage.
Kyiewu Bernard, A. Clinton, Odoi Henry et al.· Journal of Electrical System...· 0 citations
The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware, and offers viable post-quantum hardening for security-critical applications.
Soha Rawas, Mohammed Al Saleh, A. D. Samala et al.· Applied Computing and Inform...· 0 citations
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