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

We introduce QEDRA (Quantum-Entangled Decentralized Reasoning and Aggregation), the first practical framework that simultaneously addresses ultra-scarce data regimes ( nₖ < 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 ( |W₉ = 1√9∑ ᵢ₌₁⁹|1ᵢ ) to generate semantically aware noise that improves utility while providing formal (ε ,δ ) -differential privacy guarantees ( ε = 0.08 –0.17, δ = 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 ( pdep = 0.005 ), all while maintaining Tround < 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.