Applications of Quantum Computing

This module will introduce students to potential applications of near-term noisy immediate scale quantum (NISQ) computers within physics and industrial areas. The focus is here on completing simulation tasks by quantum computers, using quantum computers to solve optimization problems, or to benefit from quantum machine learning. Potential application fields in physics e.g. include quantum machine learning techniques in high energy physics such as improving tracking algorithms in interpreting detector signals or in the identification of physics beyond the Standard Model of particle physics, or improving simulation tasks in cosmology. Industrial application areas e.g. include quantum-computing assisted methods in drug discovery or within (medical) imaging. To fully understand the potential benefit of NISQ computers, this module will first introduce the basic concepts of NISQ algorithms, re-summarize the abilities of current quantum hardware, and then dive into specific algorithm areas (e.g. quantum machine learning) with concrete applications. This is helped by practical hands-on sessions on the algorithms based on recent research papers within the tutorials. Furthermore, the module discusses how current error mitigation techniques may help the near-term use of quantum computers.

After successful completion of the module the students are able to:

1. Understand the basics of current NISQ algorithms such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm and differentiate these from algorithms requiring fault-tolerant quantum computers like Grover’s algorithm.
2. Understand the different directions of quantum machine learning and how certain problems could profit from higher-dimensional kernel methods.
3. Discuss in which application areas the use of a quantum computer may be sensible or not.
4. Implement algorithms for a few example problems that may benefit from quantum computers, taken e.g. from high energy physics or medical imaging.
5. Obtain a first knowledge about current quantum hardware limitations and error mitigation techniques with respect to practical applications.

For students within the Quantum Science & Technology master program: No preconditions in addition to the requirements for the Master’s program in Physics and Quantum Science and Technology.

For other bachelor or master students in physics:

• Quantum mechanics I

• An introduction to statistics and/or machine learning techniques is very helpful

In particular students with a first contact to research projects will profit from this lecture.

This module is a lecture and exercise classes (in total 4 SWS). The teaching style will switch between blackboard presentations, possibly on a tablet computer, to present the basic concepts, and presentation slides to discuss more complicated concepts or recent research results. During the weekly practical tutorial classes integrated within the lecture, the students will work on basic examples to understand the core concepts of the lecture and practical examples from recent research papers. It is intended to gain a hands-on programming experience during the tutorials.

Blackboard / tablet computer, computer presentation slides.

• Quantum Computation & Quantum Information by M. A. Nielsen, I. J. Chuang

• Quantum machine learning: An applied approach by S. Ganguly

• Machine Learning with Quantum Computers by M. Schuld, P. Petruccione

• Noisy intermediate-scale quantum (NISQ) algorithms by K. Bharti et al.

The graded examination consists of a written exam of 60 min.

The exam will test if the student is able to identify NISQ algorithms suited for a provided application problem and has gained competencies on how to construct NISQ algorithms including fitting quantum circuits.

Additionally, general knowledge about further NISQ algorithms and current error mitigation techniques will be checked.

Participation in the exercise classes is strongly recommended since the exercises prepare for the problems of the exam and rehearse the specific competencies.