Quantum machine learning for electronic structure calculations

Rongxin Xia and Sabre Kais ()
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Rongxin Xia: Purdue University
Sabre Kais: Purdue University

Nature Communications, 2018, vol. 9, issue 1, 1-6

Abstract: 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 H2, LiH, H2O 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.

Date: 2018
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DOI: 10.1038/s41467-018-06598-z

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