Quantum Federated Learning Experiments in the Cloud with Data Encoding
Shiva Raj Pokhrel, Naman Yash, Jonathan Kua, Gang Li, Lei Pan

TL;DR
This paper explores quantum federated learning (QFL) on cloud platforms, focusing on data encoding techniques and demonstrating a proof of concept with genomic data on quantum simulators, highlighting potential for privacy-preserving quantum model training.
Contribution
It introduces a data-encoding-driven approach to QFL and provides a practical proof of concept using genomic data, addressing deployment challenges on cloud platforms.
Findings
Promising results with genomic data on quantum simulators
Identification of challenges in deploying QFL on cloud platforms
Demonstration of a data-encoding-driven QFL approach
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.
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
