QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
This work addresses the challenge of data privacy and computational efficiency for researchers and practitioners in quantum computing and machine learning, though it appears incremental as it adapts existing federated learning concepts to the quantum domain.
The authors tackled the problem of training a global quantum neural network across multiple quantum machines without centralizing local data, proposing QuantumFed, a federated learning framework for collaborative quantum training, and demonstrated its feasibility and robustness in experiments.
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.