arXiv (All)
open access
A Stable Aggregation Method for Quantum Federated Learning
Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fidelity, latency, and quantum hardware noise. Moreover, QFL is non-tri
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Stable Aggregation Method for Quantum Federated Learning#809812
- Hybrid Quantum Temporal Convolutional Networks#1041455
- ReFINE: Scheduling of Distillation and Coding for Rate-Fidelity Tradeoff in Quantum Networks#1035120
- Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients#618561
- Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage#163174
Record · ID 819808
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive