Quantum machine learning algorithms are believed to be more powerful for leveraging the power of quantum computers in solving problems of physico-chemical interests which are beyond the scope of classical computers. Such methods have recently been employed to solve quantum many-body systems and have demonstrated remarkable accuracy in solving electronic structure calculations of lattice models, molecular systems and even periodic systems. A hybrid approach involving the classical Restricted Boltzmann machine (RBM) accompanied by a quantum algorithm to obtain the desired probability distribution for the quantum state has been recently implemented as a promising tool due to its efficiency and ease of implementation on a noisy intermediate scale device. Here we extend the algorithm by implementing benchmark tests on the IBM-Q quantum computer to calculate the electronic structure of typical 2-dimensional crystal structures: hexagonal-Boron Nitride and monolayer graphene. The band structure of these systems calculated using the hybrid quantum machine leaning are in good agreement with those obtained by the conventional electronic structure calculation. This benchmark result implies that the hybrid quantum machine learning, empowered by quantum computers, could provide a new way of calculating the electronic structures of quantum many-body systems useful to understanding the properties of novel materials.

Quantum machine learning for electronic structure calculations of 2D materials on quantum computing devices

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.