2022-10-10 - 2022-10-14
Philipp Hauke
University of Trento
The capacities of quantum information processors have seen a breathtaking progress in recent years. Nevertheless, the existing machines are neither fully error corrected nor fully scalable. If we want to use quantum computers for practical benefit, it is thus of highest importance to identify target scenarios where currently existing or near-term machines can already make a difference.
Two extremely promising application fields in this context are quantum simulation and quantum optimization. In this lecture series, you will get introduced to basic and advanced algorithms for both fields. These include Trotter decomposition, qdrift, quantum annealing, quantum approximate optimization algorithm, as well as analytical methods for deriving effective Hamiltonians governing quantum simulators (Magnus expansion, degenerate perturbation theory). We will also discuss subtleties such as the role of entanglement in these algorithms as well as possibilities for error mitigation, e.g., through suitable energy penalties and dynamical decoupling. We will discuss these aspects at the example of relevant use cases from a variety of fields, e.g., quantum simulation of exotic materials and lattice gauge theories as well as quantum optimization of problems from computational biophysics.
The lecture series will be accompanied by hands-on tutorials.
