AI RESEARCH
Viability of perturbative expansion for quantum field theories on neurons
arXiv CS.LG
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ArXi:2508.03810v4 Announce Type: replace-cross Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local quantum field theories (QFTs). In the infinite neuron number limit, single-layer NNs can exactly reproduce QFT results. This paper examines the viability of this architecture for perturbative calculations of local QFTs for finite neuron number $N$ using scalar $\phi^4$ theory in $d$ Euclidean dimensions as an example.