Quantum Federated Learning Experiments in the Cloud with Data Encoding

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.