Quantum Federated Learning Experiments in the Cloud with Data Encoding

2024/05/01 by Shiva Raj Pokhrel, Naman Yash, Pokhrel, Shiva Raj +7 · 2 citations
Computer Science · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2405.00909

openalex publication_date 2024/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites
• SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization

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