Optimization of quantum algorithms for near-term quantum computers

Abstract

This thesis covers several aspects of quantum algorithms for near-term quantum computers and its applications to quantum chemistry and material science. These aspects range from error mitigation and error modeling of a quantum computing device to a measurement scheduling to extract the relevant information of a quantum state for quantum chemistry calculations.

It also presents a benchmarking study of classical optimization methods for variational quantum algorithms. Additionally, a small quantum simulation is performed on a cloud-based quantum computer to understand the bottlenecks of such infrastructure. Finally, a method to calculate energy derivatives on a quantum computer, a relevant figure for quantum chemistry calculations.