A computer-implemented method includes processing a predetermined machine learning routine of a tensor network that defines layers of tensors in the routine, which is adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor. A sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network. Thus, implementing anticommutation fermionic operator; inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction; and outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem. At least one parameter is inferred by the processed predetermined machine learning routine.