Quantum machine-learning for electronic structure calculations
Sabre Kais, Purdue University
In this talk, I will focus on quantum machine learning, particularly the Restricted Boltzmann Machine (RBM), as it emerged to be a promising alternative approach leveraging the power of quantum computers. To demonstrate its efficacy, we show its performance on calculating electronic structure of small molecular systems like LiH, H2O and also computation of band structures in 2D materials like graphene, h-BN and monolayer transition-metal di-chalcogenides which are hitherto unexplored in quantum simulations.
References:
“Quantum Machine Learning for Electronic Structure Calculations”
Xia, Rongxin; Kais, Sabre, Nature Comm. 9, 4195 DOI:10.1038/s41467-018-06598-z (2018)
“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)
“Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials”, Manas Sajjan, Shree Hari Sureshbabu, Sabre Kais, arXiv:2105.09488 (on line JACS 2021).