arXiv:2605.30075v1 Announce Type: new Abstract: Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low communication overhead, Federated Averaging (FedAvg) is the standard aggregation choice in QFL literature. However, deploying QFL on practical hardware exposes a severe double-drift phenomenon: the global model is simultaneously derailed by client drift from non-IID data and hardware bias from noisy quantum gradient estimates. In this work, we first analyze the convergenc
The continuous advancements in quantum computing hardware necessitate solutions to practical challenges like noise and distributed learning to move towards viable applications.
Addressing 'double-drift' in federated quantum learning is crucial for successful deployment of quantum AI models, potentially accelerating quantum advantage in real-world scenarios.
The ability to run decentralized quantum AI models with greater stability and accuracy is improved, making QFL a more practical approach despite inherent hardware noise.
- · Quantum computing hardware developers
- · AI researchers in distributed quantum algorithms
- · Organizations with sensitive, distributed datasets
- · Classical federated learning approaches for quantum data
- · Early, uncorrected QFL implementations
Improved reliability and broader adoption of federated quantum learning for secure data processing.
Acceleration of quantum machine learning applications in sectors like finance or healthcare where data privacy is paramount.
Potential for new quantum AI services that leverage distributed, privacy-preserving quantum computations at scale.
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Read at arXiv cs.LG