A feedforward unitary equivariant neural network. (April 2023) - Ma, Pui-Wai; Chan, T.-H. Hubert
Abstract: We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group U ( n ) . The input and output can be vectors in ℂ n with arbitrary dimension  n . No convolution layer is required in our implementation. We avoid errors due to truncated higher order terms in Fourier-like transformation. The implementation of each layer can be done efficiently using simple calculations. As a proof of concept, we have given empirical results on the prediction of the dynamics of atomic motion to demonstrate the practicality of our approach.