Quantum Federated Learning Experiments in the Cloud with Data Encoding
This addresses data privacy in collaborative quantum model training for quantum computing applications, but it is incremental as it builds on existing concepts with a proof of concept.
The paper tackles deploying Quantum Federated Learning on cloud platforms, focusing on quantum intricacies and limitations, and demonstrates a proof of concept with genomic data on simulators, showing promising results.
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.