  Considering recent advancements and successes in the development of efficient
quantum algorithms for electronic structure calculations --- alongside
impressive results using machine learning techniques for computation ---
hybridizing quantum computing with machine learning for the intent of
performing electronic structure calculations is a natural progression. Here we
report a hybrid quantum algorithm employing a restricted Boltzmann machine to
obtain accurate molecular potential energy surfaces. By exploiting a quantum
algorithm to help optimize the underlying objective function, we obtained an
efficient procedure for the calculation of the electronic ground state energy
for a small molecule system. Our approach achieves high accuracy for the ground
state energy for H$_2$, LiH, H$_2$O at a specific location on its potential
energy surface with a finite basis set. With the future availability of
larger-scale quantum computers, quantum machine learning techniques are set to
become powerful tools to obtain accurate values for electronic structures.
