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Federated Quantum Machine Learning

Journal Article · · Entropy
DOI:https://doi.org/10.3390/e23040460· OSTI ID:1777070
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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. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. 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.

Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0012704
OSTI ID:
1777070
Alternate ID(s):
OSTI ID: 1788053
Journal Information:
Entropy, Journal Name: Entropy Journal Issue: 4 Vol. 23; ISSN ENTRFG; ISSN 1099-4300
Publisher:
MDPI AGCopyright Statement
Country of Publication:
Switzerland
Language:
English

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