Summary of Quantum Federated Learning Experiments in the Cloud with Data Encoding, by Shiva Raj Pokhrel et al.
Quantum Federated Learning Experiments in the Cloud with Data Encoding
by Shiva Raj Pokhrel, Naman Yash, Jonathan Kua, Gang Li, Lei Pan
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET); Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose Quantum Federated Learning (QFL), an innovative approach that enables collaborative training of quantum models while preserving local data privacy. The authors highlight the challenges of deploying QFL on cloud platforms, including quantum intricacies and platform limitations. To address these challenges, they introduce a proof-of-concept implementation using genomic datasets on quantum simulators, demonstrating promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QFL is a new way to train quantum models together with other computers while keeping each one’s data private. This paper looks at the problems of making QFL work on cloud computing platforms and how to get around them. The authors show that their idea can be made real using special datasets for genomic research. |
Keywords
» Artificial intelligence » Federated learning
