# 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.

## Key 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.