$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
This work addresses the potential advantage of quantum neural networks in resource-limited settings, but it is incremental as it focuses on toy examples.
The paper compared equivariant quantum neural networks (EQNN) and quantum neural networks (QNN) against classical equivariant and deep neural networks for binary classification tasks, finding that EQNN and QNN performed better with fewer parameters and smaller training datasets.
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.