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Engineering Applications of Artificial Intelligence

A practical multi-layered framework for post-quantum secure machine learning

Rafik Hamza, Aziz Alotaibi, Khan Muhammad

Published Nov 15, 2025• 4 citations
Publisher
DOI
10.1016/j.engappai.2025.113044

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Purpose This study aims to develop and validate a quantum-enhanced behavioral security framework that integrates a lightweight quantum obfuscation layer into classical intrusion detection systems (IDSs). The primary objective is to harden these models against post-quantum threats, specifically model extraction and feature inversion attacks, while maintaining high detection accuracy across both traditional machine learning and deep learning architectures. Design/methodology/approach A quantum feature obfuscation layer was designed using 6-qubit parameterized circuits with angle encoding and variational ansatz to nonlinearly transform behavioral features. This layer was integrated with classical classifiers (random forest, support vector machine, logistic regression) and a deep learning model multi-layer perceptron (MLP). The framework was evaluated on UNSW-NB15 and NSL-KDD datasets using Qiskit Aer simulators in Google Colab, measuring accuracy, precision, recall, F1-score, model extraction error (MEE) and feature inversion error (FIE). Findings Quantum-assisted models maintained detection performance comparable to classical baselines, with accuracy degradation of 0.7% across all architectures. Security resilience significantly improved, with a 3–4× increase in both MEE and FIE, indicating substantially enhanced resistance to model theft and feature reconstruction. The framework demonstrated improved noise tolerance under Gaussian perturbations. The MLP achieved 92.5% accuracy (classical) versus 91.8% (quantum-assisted), with 3.3× and 3.5× improvements in MEE and FIE, respectively. Research limitations/implications The evaluation was conducted entirely on quantum simulators, not capturing real hardware noise, decoherence and fidelity limitations. The threat model assumes secrecy of quantum circuit parameters, representing a form of model-level security through obscurity. Scalability is constrained by exponential simulation costs, limiting current experiments to 6 qubits. Only feedforward neural networks (MLP) were tested; advanced architectures like CNNs, LSTMs and transformers require future validation. The framework’s resilience against adversaries with partial knowledge of the ansatz structure remains unexplored. Practical implications The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware. With only 15–25 ms inference overhead and 40–60% training overhead, it offers viable post-quantum hardening for security-critical applications. Resource-constrained environments such as edge computing nodes and IoT deployments can benefit from this approach. The demonstration that meaningful security gains (3–4× improvement) are achievable using only 6 qubits lowers the barrier for organizations to adopt quantum-assisted security measures today. Social implications As quantum computing threatens classical cryptography, protecting behavioral analytics becomes crucial for critical infrastructure, financial systems and healthcare networks. This research contributes to building resilient cyber defense mechanisms that protect sensitive data and privacy even when encryption is compromised. By democratizing access to quantum-enhanced security through cloud-based simulation platforms, the framework helps bridge the gap between institutions with varying resources, promoting more equitable cybersecurity preparedness for the post-quantum era across both developed and developing nations. Originality/value This work shifts the focus of quantum-enhanced security from pure cryptographic replacement to model-hardening, harnessing quantum state complexity as a defensive mechanism rather than pursuing quantum advantage for classification speed. It introduces a novel quantum obfuscation layer specifically designed for IDS, validated across both traditional ML and deep learning architectures. The consistent security improvements (3–4×) across classifier types confirm the approach is classifier-agnostic. The Colab-based implementation ensures reproducibility and accessibility, providing a practical foundation for future quantum-aware cyber defense research and deployment.
Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device participation, non-convex loss functions, and heterogeneous data distributions, (ii) developing a novel multi-layered post-quantum cryptographic architecture with a quantum neural network-powered adaptive mechanism that monitors conditions, evaluates threats, and adjusts parameters across three National Institute of Standards and Technology (NIST)-compliant levels. Our theoretical framework and empirical validation reveal two key insights: (i) the derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset; and (ii) findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security, while confirming that our adaptive framework effectively mitigates these overheads to preserve overall system efficiency. Specifically, the hardware experiments demonstrate that our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%. Furthermore, extensive simulations validate our theoretical analysis.....
Title: Adversarially Robust Federated Quantum Machine Learning for Healthcare: Attacks, Adaptive Defenses, and Post-Quantum Secure Aggregation Track: AI for Science Federated quantum machine learning (FQML) offers a compelling paradigm for privacy-preserving clinical AI, enabling distributed training of variational quantum circuit (VQC) models across hospital networks without centralizing sensitive patient records. While prior work has demonstrated the efficiency and accuracy of FQML frameworks, the security of these systems under adversarial conditions remains entirely unaddressed, a gap explicitly identified in foundational FQML research. An empirical adversarial security evaluation of FQML in a healthcare setting is presented here, directly addressing the challenge of building reliable and trustworthy AI systems identified as a central theme of NYSDS 2026. Hybrid VQC clients are implemented using PennyLane on the liver cirrhosis staging dataset distributed across twenty simulated hospital nodes under realistic non-IID Dirichlet partitioning. The system is subjected to five attack types including data poisoning, model poisoning, backdoor injection, man-in-the-middle interception, and a novel adaptive adversary escalation scenario. Protection is provided by a multi-layered adaptive defense stack comprising exponential moving average trust scoring, multi-signal anomaly detection using z-score, variance, and kurtosis signals, adaptive trimmed mean aggregation, per-client differential privacy scheduling, and CRYSTALS-Kyber-768 post-quantum key encapsulation implemented as ML-KEM-768 under NIST FIPS 203 for gradient transmission security. Across three independent experimental runs, the proposed defended system achieves 82.84% mean test accuracy and a security detection AUC of 0.96, compared to 36.0% accuracy for undefended quantum FL under identical adversarial conditions, confirming that FQML without defense is functionally non-operational in adversarial healthcare environments. The defended quantum system AUC of 0.96 exceeds the classical defended baseline of 0.94, demonstrating that classical adaptive defenses transfer effectively to VQC parameter updates and produce superior anomaly detection. Backdoor and model poisoning detection rates reach 100% and 98% respectively. A novel finding identifies a critical operating window of 4 to 12 federated clients within which the system achieves stable performance, with direct implications for practical hospital network deployment. Post-quantum encryption via Kyber-768 provides provable forward security against harvest-now-decrypt-later threats while demonstrably improving man-in-the-middle detection rates. These results establish that adversarially robust federated quantum ML is achievable and motivate the design of quantum-native defense mechanisms for next-generation clinical AI systems.