Fermionic Wave Functions from Neural-Network Constrained Hidden States

Understand

We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems.

• This family consists of Slater determinants in an augmented Hilbert space involving "hidden" additional fermionic degrees of freedom.
• These determinants are projected onto the physical Hilbert space through a constraint which is optimized, together with the single-particle orbitals, using a neural network parametrization.
• This construction draws inspiration from the success of hidden particle representations but overcomes the limitations associated with the mean-field treatment of the constraint often used in this context.