# Sabre Kais Group

## Quantum Information and Quantum Computation

# Quantum Machine Learning for Electronic Structure Calculations

Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations — alongside similarly impressive results using machine learning techniques for computation — hybridizing quantum computing with machine learning for the intent of perform electronic structure calculations is a natural progression. We have developed a hybrid quantum algorithm employing a quantum restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. The Boltzmann machine trains parameters within an Ising-type model which exists in thermal equilibrium. By exploiting a quantum algorithm to optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a system.

**Quantum Machine Learning for Electronic Structure Calculations**

Xia, Rongxin; Kais, Sabre

Nature Comm. 9, 4195 DOI:10.1038/s41467-018-06598-z (2018)| Article PDF**Quantum Machine-Learning for Eigenstate Filtration in Two Dimensional Materials**

Manas Sajjan, Shree Hari Sureshbabu and Sabre Kais

J. Am. Chem. Soc. 2021, DOI: 10.1021/jacs.1c06246: Article PDF.**Quantum machine learning for chemistry and physics**

Manas Sajjan, Junxu Li, Raja Selvarajan, Shree Hari Sureshbabu, Sumit Suresh Kale,

Rishabh Gupta, Vinit Singh and Sabre Kais

Chemical Society Reviews, 2022, 51, 6475 (Advance Article)

DOI: 10.1039/D2CS00203E: Article PDF**Implementation of Quantum Machine Learning for Electronic Structure**

Calculations of Periodic Systems on Quantum Computing Devices

Sureshbabu, Shree Hari; Sajjan, Manas; Oh, Sangchul; Kais, Sabre

J. Chem. Inf. 61, 2667-2674 (2021) Link to PDF