We introduce QEDRA (Quantum-Entangled Decentralized Reasoning and Aggregation), the first practical framework that simultaneously addresses ultra-scarce data regimes (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(n_k < 20\)</EquationSource> </InlineEquation>), 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 (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(|\textrm{W}_9\rangle = \frac{1}{\sqrt{9}}\sum _{i=1}^9|1_i\rangle \)</EquationSource> </InlineEquation>) to generate semantically aware noise that improves utility while providing formal <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\((\epsilon ,\delta )\)</EquationSource> </InlineEquation>-differential privacy guarantees (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\epsilon = 0.08\)</EquationSource> </InlineEquation>–0.17, <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\delta = 10^{-16}\)</EquationSource> </InlineEquation>). 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 (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(p_{\text {dep}} = 0.005\)</EquationSource> </InlineEquation>), all while maintaining <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(T_{\text {round}} < 2.4\)</EquationSource> </InlineEquation> s latency through BB84-QKD-secured channels. Critically, QEDRA is engineered for real-world deployment on IBM 127-qubit Eagle processors (<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(QV=128\)</EquationSource> </InlineEquation>) with seamless classical fallback when entanglement fidelity drops below <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(F < 0.98\)</EquationSource> </InlineEquation>. 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.

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

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2026年5月26日

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Soufiane Ben Othman,
Obaid Ali

摘要

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.