The Neural Networks with Tensor Weights and the Corresponding Fermionic Quantum Field Theory
This work extends neural network quantum field theory beyond bosonic theories, potentially aiding quantum simulation and lattice field theory, but it is incremental as it builds on prior NN-QFT frameworks.
The paper tackled the problem of connecting neural networks to fermionic quantum field theory by demonstrating that complex-valued neural networks with tensor-valued weights generate fermionic quantum fields, establishing an explicit mapping at the level of correlation functions and generating functionals.
In this paper, we establish a theoretical connection between complex-valued neural networks (CVNNs) and fermionic quantum field theory (QFT), bridging a fundamental gap in the emerging framework of neural network quantum field theory (NN-QFT). While prior NN-QFT works have linked real-valued architectures to bosonic fields, we demonstrate that CVNNs equipped with tensor-valued weights intrinsically generate fermionic quantum fields. By promoting hidden-to-output weights to Clifford algebra-valued tensors, we induce anticommutation relations essential for fermionic statistics. Through analytical study of the generating functional, we obtain the exact quantum state in the infinite-width limit, revealing that the parameters between the input layer and the last hidden layer correspond to the eigenvalues of the quantum system, and the tensor weighting parameters in the hidden-to-output layer map to dynamical fermionic fields. The continuum limit reproduces free fermion correlators, with diagrammatic expansions confirming anticommutation. The work provides the first explicit mapping from neural architectures to fermionic QFT at the level of correlation functions and generating functional. It extends NN-QFT beyond bosonic theories and opens avenues for encoding fermionic symmetries into machine learning models, with potential applications in quantum simulation and lattice field theory.