Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices

TL;DR

This paper demonstrates the implementation of hybrid quantum machine learning algorithms on IBM-Q quantum computers to accurately compute electronic structures of 2D periodic systems like graphene and hexagonal-Boron Nitride, showing promising results.

Contribution

It presents a practical implementation and benchmarking of hybrid quantum machine learning for electronic structure calculations of periodic systems on real quantum hardware.

Findings

Band structures agree with conventional calculations

Hybrid approach is efficient and easy to implement

Quantum computers can effectively simulate periodic systems

Abstract

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 2-dimensional crystal structures: hexagonal-Boron Nitride and graphene. The band structures of these systems calculated using the hybrid quantum machine learning 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.