Quantum Machine Learning for Electronic Structure Calculations

TL;DR

This paper introduces a hybrid quantum machine learning algorithm using a restricted Boltzmann machine to accurately compute molecular potential energy surfaces, demonstrating promising results for small molecules and future potential for larger systems.

Contribution

The paper presents a novel hybrid quantum algorithm that combines quantum computing with machine learning to efficiently calculate electronic ground state energies.

Findings

Achieved high accuracy for H₂, LiH, H₂O molecules.

Utilized quantum algorithms to optimize the objective function.

Demonstrated potential for scaling with larger quantum computers.

Abstract

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.