Applied Quantum Algorithms

This course introduces you to the field of quantum algorithms and their applications. We will discuss state-of-the-art quantum algorithms for solving linear systems of equations and continuous as well as combinatorial optimisation problems. 

The focus of this course is on quantum algorithms that are of practical interest. This means that we will not only study the general working principles of these algorithms and their theoretical properties but also discuss the necessary pre- and post-processing steps (e.g. the encoding of system matrices into quantum-amenable format and the interpretation of probabilistic results) to integrate quantum algorithms into end-to-end quantum(-assisted) applications. The treated quantum algorithms are the HHL algorithm, the Quantum Approximate Optimization Algorithm, variational quantum algorithms, some basic Quantum Machine Learning approaches, or similar. The course will make use of various simulators that exist for these applications. Depending on time and interest, we might also cover how such simulators work "under the hood" and treat various classical simulation algorithms for quantum circuits such as tensor networks and the stabiliser formalism.

The course will consist of two parts, which run in parallel:

• an introduction into the subject through lectures and hands-on assignments;

• a 2-3 person project to apply the theory and methods taught in the lectures.

The final grade of the course consists of the following components:

• Individual Assignment 1 (weighting 20%)

• Individual Assignment 2 (weighting 20%)

• Project Presentation (weighting 60%)

Final grade calculation = 0.2 * Individual Assignment 1 + 0.2 * Individual Assignment 2 + 0.6 * Project Presentation

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Individual Assignment 1: Repair opportunity

• Individual Assignment 2: Repair opportunity

• Project Presentation: Resit opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).