Quantum Federated Learning Experiments in the Cloud with Data Encoding

Autor:	Pokhrel, Shiva Raj, Yash, Naman, Kua, Jonathan, Li, Gang, Pan, Lei
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Emerging Technologies Quantum Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: SIGCOMM 2024, Quantum Computing, Federated Learning, Qiskit
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.00909 Zobrazit plný text záznamu View this record from Arxiv