Fermions and supersymmetry in neural network field theories
Abstract
Abstract We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.
Fields of Study & Subjects
Quantum many-body systems
Quantum Computing Algorithms and Architecture
Neural Networks and Applications
Publication details
- Journal / source
- Machine Learning Science and Technology
- Volume
- 7
- Issue
- 4
- Pages
- 045070–045070
- Publication date
- 1 August 2026
- Language
- EN