Project Details
Description
Modeling the thermodynamic properties of molecules is an important subfield of chemistry, with applications in the discovery and design of pharmaceuticals, agrochemicals, and advanced materials. The properties that we are interested in are the enthalpy, entropy, and heat of formation of isolated molecules, which can in turn be used to predict macroscopic properties such as solubility and phase equilibria. Experimentally, it is difficult, costly, and time-consuming to evaluate these quantities, especially for a large ensemble of different
molecules of interest. For this reason, in-silico methods are routinely employed in industry for tackling this problem. State-of-the-art approaches involve performing ab-initio quantum chemistry calculations, most often with post-Hartree-Fock methods. For these calculations, there is a trade-off between the accuracy of the results obtained and the computational cost incurred. For molecules larger than 50 atoms, only density functional theory based approaches remain viable, but their performance is seldom reliable for molecules in solution.
The main topic that this project will investigate is how quantum computing can be used to perform quantum chemistry calculations for solvation modeling. We will do this by pursuing
three main research questions:
1) How can we improve the performance of quantum algorithms for quantum chemistry calculations and utilize them for thermodynamic property prediction?
2) How can quantum computing be integrated into existing classical computational pipelines and how should software be designed for efficient hybrid quantum-classical computations?
3) What adaptations and auxiliary techniques are needed to ensure the compatibility of these quantum algorithms with quantum hardware, both existing and under development?
The project includes an industrial collaboration with Molecular Quantum Solutions (MQS), which is a company based in Denmark. The company is specialized in solvation modeling and has ongoing research activities in quantum algorithms.
molecules of interest. For this reason, in-silico methods are routinely employed in industry for tackling this problem. State-of-the-art approaches involve performing ab-initio quantum chemistry calculations, most often with post-Hartree-Fock methods. For these calculations, there is a trade-off between the accuracy of the results obtained and the computational cost incurred. For molecules larger than 50 atoms, only density functional theory based approaches remain viable, but their performance is seldom reliable for molecules in solution.
The main topic that this project will investigate is how quantum computing can be used to perform quantum chemistry calculations for solvation modeling. We will do this by pursuing
three main research questions:
1) How can we improve the performance of quantum algorithms for quantum chemistry calculations and utilize them for thermodynamic property prediction?
2) How can quantum computing be integrated into existing classical computational pipelines and how should software be designed for efficient hybrid quantum-classical computations?
3) What adaptations and auxiliary techniques are needed to ensure the compatibility of these quantum algorithms with quantum hardware, both existing and under development?
The project includes an industrial collaboration with Molecular Quantum Solutions (MQS), which is a company based in Denmark. The company is specialized in solvation modeling and has ongoing research activities in quantum algorithms.
| Acronym | SQA Round 9 |
|---|---|
| Status | Active |
| Effective start/end date | 1/04/24 → 31/03/28 |