Analysis of a hybrid quantum network for classification tasks

Abstract

In the era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here, we report about an analysis, where a hybrid quantum-classical network is applied to classify non-trivial datasets (finance and MNIST data). In comparison to a pure classical network, we find an advantage when looking at several performance measures. As in classical machine learning, problems around overfitting the dataset arise. Therefore, we explore different possibilities to regularise the network.