# Freiburg 2019 – wissenschaftliches Programm

## Sitzungen | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

# FM: Fall Meeting

## FM 23: Quantum & Information Science: Neural Networks, Machine Learning, and Artificial Intelligence I

### FM 23.3: Talk

### Montag, 23. September 2019, 17:30–17:45, 3043

**Machine learning for quantum chemistry with quantum computers** — •Tomislav Piskor — HQS Quantum Simulations, Karlsruhe, Germany

Simulating chemical systems is a major field of interest not only for the pharma and chemistry, but also for the automotive industry. One such example is the simulation of functional groups of a large molecule or proteine, which can be useful for the development of new medicine. In order to get the exact ground state, we might use quantum computers in the future. However, every call to a quantum computer will be relatively expensive, making high-throughput simulations with quantum computers unfeasible.

To bypass this, a few single point calculations are determined with an expensive method and then extended to more conformations with, e.g., machine learning methods. The less time-consuming method of choice is density functional theory (DFT). Our approach is to take a hybrid functional, such as B3LYP, which consists of three exchange and two correlation functionals. Each of these functionals has a certain weight which can be modified. Using, for example, a root finding optimizer the functional parameters are optimized in such a way that the energy and the corresponding nuclear gradients of the time-consuming method match the DFT results. In this work, we use coupled-cluster methods with single and double excitations (CCSD) and complete active space self-consistent-field (CASSCF) methods as our computationally expensive methods.