Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
Paper recorded by Signals 4 on 2026-08-28 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31
Category: cs.LG · 机器学习 · first seen 2026-08-31
Abstract
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 exten
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Cite this page: Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients: the #215 most recent of 215 cs.LG papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/quantum-federated-learning-based-on-bures-uhlmann-geometry-for-heterogeneous-noi.html
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