Quantum
machine learning algorithms, the extensions of machine
learning to quantum regimes, are believed to be more powerful as they
leverage the power of quantum properties. Quantum machine learning
methods have been employed to solve quantum many-body systems and
have demonstrated accurate electronic structure calculations of lattice
models, molecular systems, and recently periodic systems. A hybrid
approach using restricted Boltzmann machines and a quantum algorithm
to obtain the probability distribution that can be optimized classically
is a promising method due to its efficiency and ease of implementation.
Here, we implement the benchmark test of the hybrid quantum machine
learning on the IBM-Q quantum computer to calculate the electronic
structure of typical two-dimensional crystal structures: hexagonal-boron
nitride and graphene. The band structures of these systems calculated
using the hybrid quantum machine learning approach are in good agreement
with those obtained by the conventional electronic structure calculations.
This benchmark result implies that the hybrid quantum machine learning
method, empowered by quantum computers, could provide a new way of
calculating the electronic structures of quantum many-body systems.