首页|A quantum federated learning framework for classical clients
A quantum federated learning framework for classical clients
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Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local data.However,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing capabilities.This raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this question.In each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of clients.Specifically,the server constructs a classical representation of the QML model and transmits it to the clients.The clients encode their local data onto observables and use this classical representation to calculate local gra-dients.These local gradients are then utilized to update the parameters of the QML model.We evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST dataset.Our framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.
State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China
Department of Physics,The University of Westem Australia,Perth,W4 6009,Australia
School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Natural Science FoundationChina Scholarship Council
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