Quantum Federated Learning for Image Classification

Leo Sünkel, Philipp Altmann, Michael Kölle, Thomas Gabor

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