Fig. 1: Schematic illustration of the relationship between the formal computational complexity and accuracy in various first-principles calculation methods for solid systems.
From: Solving quasiparticle band spectra of real solids using neural-network quantum states
Our goal is to demonstrate that the variational calculation using neural-network-based ansatz can readily describe both weakly and strongly correlated electronic structures with moderate number of variational parameters, i.e., computational cost. We denote the full configuration interaction (FCI) method by the black square, whereas the Hartree–Fock (HF) and post-HF calculation methods are indicated by blue squares: the second-order Møller–Plesset perturbation theory (MP2), the coupled-cluster singles and doubles (CCSD), and CCSD with perturbative triple excitations (CCSD(T)). Also, the green squares indicate methods based on the Density Functional Theory (DFT): the DFT and DFT-based Random Phase Approximation (RPA). The number of orbitals at each k-point is denoted as N and the total number of k-points as Nk. Note that this is a qualitative (approximate) illustration, which will vary from case to case.
