Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Published in Axioms, 2024
Recommended citation: Dong Z, Comajoan Cara M, Dahale GR, Forestano RT, Gleyzer S, Justice D, Kong K, Magorsch T, Matchev KT, Matcheva K, et al. ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. Axioms. 2024; 13(3):188. https://doi.org/10.3390/axioms13030188
Abstract
This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Neural Networks (DNNs). We evaluate the performance of each network with three two-dimensional toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training dataset. 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.