Accelerating quantum variational methods using Bayesian optimization

Quantum computers hold the capability to solve problems that are out of the computational reach of classical computers. The race to finding and testing such problems on a smaller scale is currently on. Variational methods are one of the candidates leading this race. These methods are hybrid, meaning that they require quantum as well as classical computers working together. As the size of the problems increases and becomes relevant to the current needs of society, they also become more challenging to solve. This project is about improving the variational methods by combining them with a special technique known as Bayesian optimisation. The combination will accelerate variational optimisation by reducing the required quantum computational resources. Ultimately, this project will contribute towards solving the problems of interest to society, e.g. simulating large molecules, faster and more reliably once a powerful enough quantum computer is operational.

Faculty Supervisor:

Roman Krems

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Physics

Sector:

Education

University:

The University of British Columbia

Program: