Authors:
Leo Sünkel
;
Philipp Altmann
;
Michael Kölle
and
Thomas Gabor
Affiliation:
Institute for Informatics, LMU Munich, Germany
Keyword(s):
Federated Learning, Quantum Machine Learning, Distributed Learning, Quantum Networks.
Abstract:
Federated learning is a technique in classical machine learning in which a global model is collectively trained by a number of independent clients, each with their own datasets. Using this learning method, clients are not required to reveal their dataset as it remains local; clients may only exchange parameters with each other. As the interest in quantum computing and especially quantum machine learning is steadily increasing, more concepts and approaches based on classical machine learning principles are being applied to the respective counterparts in the quantum domain. Thus, the idea behind federated learning has been transferred to the quantum realm in recent years. In this paper, we evaluate a straightforward approach to quantum federated learning using the widely used MNIST dataset. In this approach, we replace a classical neural network with a variational quantum circuit, i.e., the global model as well as the clients are trainable quantum circuits. We run three different exper
iments which differ in number of clients and data-subsets used. Our results demonstrate that basic principles of federated learning can be applied to the quantum domain while still achieving acceptable results. However, they also illustrate that further research is required for scenarios with increasing number of clients.
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