QFED-MAZARI: A Unified Architecture for Privacy-Preserving Quantum Federated Learning with the Mazari Quantum Ordering

Quantum federated learning (QFL) promises to combine the computational advantages of quantum machine learning with the privacy benefits of federated architectures. However, existing QFL approaches fundamentally misapply classical federated learning techniques to quantum systems, treating quantum parameters as classical vectors and ignoring the geometric structure of unitary operators.

We introduce QFED-MAZARI, the first unified architecture that treats QFL as an intrinsically quantum problem. Our framework comprises four integrated innovations: Manifold Unitary Aggregation (MUA) using Baker-Campbell-Hausdorff expansions on SU(2^n) Lie groups, Quantum Differential Privacy via Controlled Decoherence (QDP-CD), Recursive Quantum Aggregation Trees (RQAT) achieving O(log N) communication complexity, and Distributed Quantum Error Mitigation (DQEM).

Crucially, we establish the Mazari Quantum Ordering—QDP→MUA→DQEM—the optimal sequence creating compounding advantages analogous to the classical Y.I.N. Ordering. Theoretical analysis proves convergence guarantees with O(poly(n,N,T)) complexity versus O(2^n) for naive approaches. Experimental evaluation demonstrates 2–4% accuracy improvements over parameter averaging, 40–50% communication reduction, and privacy costs of 3–5% versus 5–10% for classical methods.

Patent Status: U.S. Patent Application No. 19/417,196 (CIP filed December 11, 2025) and U.S. Provisional Patent No. 63/939,279 (filed December 12, 2025).

The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, embodying the principle: Your Information Never leaves your control.