QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when mu...

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Bibliographic Details
Published in2021 IEEE Global Communications Conference (GLOBECOM) pp. 1 - 6
Main Authors Xia, Qi, Li, Qun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2021
Subjects
Collaboration
Collaborative work
Computational modeling
Deep learning
federated learning
Neural networks
Quantum computing
quantum neural networks
Training
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Summary:With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.
DOI:10.1109/GLOBECOM46510.2021.9685012