ℤ<sub>2</sub> × ℤ<sub>2</sub> Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Abstrak
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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="double-struck">Z</mi><mn>2</mn></msub><mo>×</mo><msub><mi mathvariant="double-struck">Z</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
Topik & Kata Kunci
Penulis (11)
Zhongtian Dong
Marçal Comajoan Cara
Gopal Ramesh Dahale
Roy T. Forestano
Sergei Gleyzer
Daniel Justice
Kyoungchul Kong
Tom Magorsch
Konstantin T. Matchev
Katia Matcheva
Eyup B. Unlu
Akses Cepat
- Tahun Terbit
- 2024
- Sumber Database
- DOAJ
- DOI
- 10.3390/axioms13030188
- Akses
- Open Access ✓