Quantum Computing based density functionals for fast and accurate materials and chemistry simulations

Quantum computers have the potential to lead to a transformative increase in the available computational power. The size and quality of the quantum computers available today are increasing rapidly, and advancements in ability to implement quantum error correction are getting us closer to have quantum computers which will be able to outperform even the best existing classical computers. One of the most promising improvements offered by quantum computers is the ability to accurately simulate atomic scale physical systems, which is a challenging task for classical computers. Various algorithms are being developed today that aim to make use of this advantage that is offered by quantum computers.

Density functional theory (DFT) is a core classical computing method for product development across industries such as pharmaceuticals, chemicals, and materials. For instance, DFT can be used to model and evaluate the properties of new materials, drugs, catalysts, etc. to complement expensive developments in the lab. Compared to alternative methods, DFT is the only approach fast enough for large systems comprising metals and molecules as required in many applications. However, approximations made in the DFT algorithms used today make them unsuitable for a lot of important problems. Improvements in DFT can lead to the development of better batteries, more efficient catalysts, faster and more reliable drug and vaccine candidate assessments, and greener chemical production.

Recently, machine learning (ML) methods have been demonstrated to lead to improvements in DFT. However, even the ML + DFT methods rely on a large number of accurate simulations of the physical systems, which are challenging to do with classical computers. In this project, we aim to harness the superior ability of quantum computers to perform accurate simulations in order to improve DFT. Our approach will combine the advantages provided by quantum computers and ML in order to significantly improve the DFT method that is widely used in various industries.

This project combines the expertise in machine learning, quantum computing, DFT, and modelling in industrial settings of InstaDeep, NPL, Atos UK, and Johnson Matthey.