Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.
Quantum Federated Learning Experiments in the Cloud with Data Encoding
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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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New Insights on Unfolding and Fine-tuning Quantum Federated Learning
Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.