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

Journal Article · 12 March 2024 · Axioms

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

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.

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.

• 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