Federated Quantum Machine Learning
This work addresses scaling and privacy challenges in quantum machine learning for researchers and practitioners, though it is incremental as it adapts classical federated learning to a quantum context.
The paper tackled the problem of distributed training in quantum machine learning by introducing a federated learning framework for hybrid quantum-classical models, achieving similar model accuracies and significantly faster training times compared to non-federated approaches.
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.