Quantum Federated Learning Experiments in the Cloud with Data Encoding
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911862749134848 |
|---|---|
| author | Pokhrel, Shiva Raj Yash, Naman Kua, Jonathan Li, Gang Pan, Lei |
| author_facet | Pokhrel, Shiva Raj Yash, Naman Kua, Jonathan Li, Gang Pan, Lei |
| contents | Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00909 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Federated Learning Experiments in the Cloud with Data Encoding Pokhrel, Shiva Raj Yash, Naman Kua, Jonathan Li, Gang Pan, Lei Machine Learning Emerging Technologies Quantum Physics Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results. |
| title | Quantum Federated Learning Experiments in the Cloud with Data Encoding |
| topic | Machine Learning Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2405.00909 |