  We present a novel method for initializing layers of tensorized neural
networks in a way that avoids the explosion of the parameters of the matrix it
emulates. The method is intended for layers with a high number of nodes in
which there is a connection to the input or output of all or most of the nodes.
The core of this method is the use of the Frobenius norm of this layer in an
iterative partial form, so that it has to be finite and within a certain range.
This norm is efficient to compute, fully or partially for most cases of
interest. We apply the method to different layers and check its performance. We
create a Python function to run it on an arbitrary layer, available in a
Jupyter Notebook in the i3BQuantum repository:
https://github.com/i3BQuantumTeam/Q4Real/blob/e07c827651ef16bcf74590ab965ea3985143f891/Quantum-Inspired%20Variational%20Methods/Normalization_process.ipynb
