Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware heterogeneity inherent to noisy quantum devices. Utilizing the quantum geometric tensor is a natural remedy, yet pure-state approaches and diagonal approximations discard the correlations that encode parameter incompatibility. To address this, we extend the parameter-space geometry to the mixed states that noisy clients actually prepare. The real part of the resulting mixed-state geometric tensor is the Bures metric, which measures how fast the phy
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- PossiblePossibly related (embedding) · 53%New Framework Uses Quantum Geometry to Help Quantum AI Systems Remember What They Learn - The Quantum Insider →
- PossiblePossibly related (embedding) · 52%Hybrid Quantum-neural Network Beats Classical Machine Learning - quantumzeitgeist.com →
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Quantum Federated Learning Based on Bures--Uhlmann Geometry ” ≈ “amitness/learning””
- LinkedLinked via arxiv author · 85%Haruki Emori →
“Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients”
- LinkedLinked via arxiv author · 85%Masaki Uchihara →
“Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients”
- LinkedLinked via arxiv author · 85%Yuuki Tokunaga →
“Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients”