ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

Dong, Zhongtian; Comajoan Cara, Marçal; Dahale, Gopal Ramesh; Forestano, Roy T.; Gleyzer, Sergei; Justice, Daniel; Kong, Kyoungchul; Magorsch, Tom; Matchev, Konstantin T.; Matcheva, Katia; Unlu, Eyup B.

doi:10.3390/axioms13030188

ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

Journal Article · Wed Mar 13 00:00:00 EDT 2024 · Axioms

DOI:https://doi.org/10.3390/axioms13030188· OSTI ID:2323569

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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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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231; SC0012447; SC0022148; SC0024407

OSTI ID:: 2323569

Journal Information:: Axioms, Journal Name: Axioms Journal Issue: 3 Vol. 13; ISSN 2075-1680; ISSN AXIOB8

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

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