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Cognitive Computation

A Neuro-Cognitive Quantum Architecture for Trustworthy AI in Ultra-Scarce Medical Environments

Soufiane Ben Othman, Obaid Ali

Published May 26, 2026• 0 citations
Publisher

Abstract

We introduce QEDRA (Quantum-Entangled Decentralized Reasoning and Aggregation), the first practical framework that simultaneously addresses ultra-scarce data regimes ( $$n_k < 20$$ ), quantum-scale security threats, and ethical requirements in healthcare AI. Unlike conventional federated learning systems that sacrifice privacy for utility, QEDRA leverages 9-qubit W-state entanglement ( $$|\textrm{W}_9\rangle = \frac{1}{\sqrt{9}}\sum _{i=1}^9|1_i\rangle $$ ) to generate semantically aware noise that improves utility while providing formal $$(\epsilon ,\delta )$$ -differential privacy guarantees ( $$\epsilon = 0.08$$ –0.17, $$\delta = 10^{-16}$$ ). QEDRA’s NISQ-compatible architecture integrates quantum physics with neuro-symbolic reasoning through four key innovations: (1) Quantum-Entangled Differential Privacy (QEDP) that breaks the classical privacy-utility trade-off; (2) Neuro-Symbolic Swarm Intelligence with Quantum-Entangled PSO (QEPSO) for multimodal fusion; (3) Privacy-Aware LLM Fine-Tuning with symbolic guardrails; and (4) Verifiable Ethical Governance via Quantum Multi-Party Computation. Validated on MIMIC-IV, QEDRA achieves 87.0% F1-score (45% higher than FedAvg), 0.4% attack success rate, and 99% fault recovery under decoherence ( $$p_{\text {dep}} = 0.005$$ ), all while maintaining $$T_{\text {round}} < 2.4$$ s latency through BB84-QKD-secured channels. Critically, QEDRA is engineered for real-world deployment on IBM 127-qubit Eagle processors ( $$QV=128$$ ) with seamless classical fallback when entanglement fidelity drops below $$F < 0.98$$ . By co-designing quantum circuits with classical resilience, QEDRA delivers provable trustworthiness without performance compromise, establishing a new standard for quantum-resilient, ethically grounded AI in resource-constrained medical environments.

DOI
10.1007/s12559-026-10599-2

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The rapid proliferation of wireless body area networks (WBANs) has intensified the need for architectures that simultaneously guarantee quantum-resistant security, patient privacy, and clinical-grade diagnostic fidelity. This paper introduces quantum-enhanced privacy aggregation (QEPA), a framework that establishes a new state-of-the-art in secure IoMT by fulfilling the “Triple Constraint” of Privacy, Robustness, and Diagnostic Fidelity, albeit with a critical dependency on line-of-sight quantum channels. QEPA’s defense-in-depth architecture integrates four synergistic innovations: (i) BB84 Quantum Key Distribution providing information-theoretic security ([Formula: see text]), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over [Formula: see text] enabling exact integer arithmetic with [Formula: see text] complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against [Formula: see text] compromised nodes; and (iv) SHAP-based explainable AI achieving 97.8% attribution fidelity against cardiologist-validated ground truth. Evaluated on a large-scale WBAN with [Formula: see text] sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = [Formula: see text], while providing unprecedented efficiency: 8.05× lower latency (31.8 ms) and 5.96× lower energy (3.32 mJ) versus Paillier, alongside 17.1× communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining [Formula: see text] differential privacy and resisting quantum attacks with [Formula: see text]-operation hardness. While the current implementation’s reliance on line-of-sight QKD limits deployment in high-mobility clinical environments, these results establish QEPA as quantum-safe framework suitable for FDA-cleared life-critical medical telemetry in controlled settings, with clear pathways toward non-line-of-sight quantum networking.
AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from 3 TNMP3\,TNMP3TNMP, the cost of pure Centralized QFL, to {3t+2(T−t)} NMP\{3t + 2(T - t)\}\,NMP{3t+2(T−t)}NMP over TTT rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
Abstract The healthcare industry frequently handles sensitive and proprietary data, and due to strict privacy regulations, it is often reluctant to share it directly. In today’s context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to finance. In this work, we propose a federated learning framework based on quantum tensor networks (QTNs) that takes advantage of the principles of many-body quantum physics. Currently, there are no known classical tensor networks (TNs) implemented in federated settings. Furthermore, we investigated the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis to ensure the security of sensitive data across healthcare institutions. Experiments on popular medical image datasets show that the federated quantum tensor network (FedQTNs) model achieved a mean receiver-operator characteristic area under the curve of 91%–98%, outperforming several state-of-the-art federated learning methods. Moreover, QTN models require fewer parameters in FL settings compared to traditional classical models, which often suffer from over-parameterization. This reduction in parameters not only improves the efficiency of the communication process but also significantly decreases data consumption during training. As a result, QTN models facilitate a more effective and resource-efficient approach to training in decentralized environments with limited communication bandwidth. The FedQTN models demonstrate a smaller performance drop even when using strong differential privacy settings, maintaining higher accuracy compared to classical models under similar privacy constraints. Experimental results demonstrate that the quantum federated global model, consisting of highly entangled TN structures, showed better generalization and robustness and achieved higher testing accuracy, surpassing the performance of locally trained clients under unbalanced data distributions among healthcare institutions.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
The development of the application of making use of Wireless Body Area Networks (WBANs) in medically created systems has presented fresh security concerns, in particular with the advent of quantum computing threatening the conventional cryptographic ideas. To proposes a new model Scalable Quantum-Intelligent Blockchain Framework (SQIBF) and a Deep Quantum Neural Architecture (DQNA) to privacy-preserving healthcare communications. The proposed framework is a synergistic approach of post-quantum lattice-based cryptography, in combination with quantum key protocols distribution schemes with federated quantum machine learning to develop multi-layered security infrastructure of the body sensor networks. We introduce Quantum Enhanced Consensus Mechanism (QECM), which deploys quantum random number generation to deliver Byzantine fault tolerance, and which can withstand symptoms of quantum-enabled parties by 97.5. The DQNA implements variational quantum gate scale used to detect textbook anomalies and categorize confidential medical data variational quantum circuit with a parameterized scale according to parameters to support the 94.3 percent mark on benchmark medical datasets with a differential privacy clarification. Experimental Assessment Simulations on simulated quantum systems and experimental WBAN testbeds have shown that it, SQIBF, can decrease the key establishing latency by approximately 40 percent when compared to classical blockchain methods using resource optimizing biosensors resource exploratory consumptions. Its model ensures the compliance of the HIPAA and GDPR requirements and homomorphic encryption layers with zero-knowledge proofs. Scalability of performance benchmarking implies up to 10,000 active sensor nodes and sub-millisecond finality of transaction. The present work introduces an end-to-end quantum resistant system to facilitate the muchneeded transitional process of quantum computing, distributed ledger technology, and health care IoT and establishes a platform of safe telemedicine infrastructure in the next generation.