Many-body calculations for periodic materials via quantum machine learning

Understand

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.