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

Computational Physics;Quantum Physics