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 physical state changes under parameter variation, and the imaginary part is the mean Uhlmann curvatu...
| Authors | Haruki Emori, Masaki Uchihara, Yuuki Tokunaga |
|---|---|
| Primary category | quant-ph |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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