Quantum machine learning for electronic structure calculations.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 30305624.
- Also identified by DOI 10.1038/s41467-018-06598-z and PMC identifier 6180079.
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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<sub>2</sub>, LiH, H<sub>2</sub>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.