DOE OSTI · 2323569
ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
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 Z2×Z2 EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
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Dong, Zhongtian (ORCID:0000000210003454), Comajoan Cara, Marçal (ORCID:0009000126263752), Dahale, Gopal Ramesh (ORCID:0009000581161950), Forestano, Roy T. (ORCID:0000000203552076), Gleyzer, Sergei (ORCID:0000000262228102), Justice, Daniel (ORCID:0000000154502207), Kong, Kyoungchul (ORCID:0000000345157303), Magorsch, Tom (ORCID:0000000338900066), Matchev, Konstantin T. (ORCID:0000000341829096), Matcheva, Katia (ORCID:000000033074998X), Unlu, Eyup B. (ORCID:0000000266836463). 2024-03-13. ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. https://doi.org/10.3390/axioms13030188
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