Many-body calculations for periodic materials via restricted Boltzmann machine-based VQE

A state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called can be powerful approach for solving many-body problems. However, the research scope in field was mainly limited to organic molecules simple lattice models. Here, we propose workflow of learning applications periodic systems on basis an effective model construction from first principles. The band structures Hubbard graphene with mean-field approximation are calculated as benchmark, eigenvalues show good agreement exact diagonalization results within few meV by employing transfer technique learning. present scheme has potential solve problems quickly correctly using computer.