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