Applications of Quantum Computing
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
NAT7022 is a semester module in English language at Master’s level which is offered every semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Experimental Quantum Science & Technology in M.Sc. Quantum Science & Technology
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Responsible coordinator of the module NAT7022 is Jeanette Lorenz.
Content, Learning Outcome and Preconditions
After successful completion of the module the students are able to:
- 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.
- Understand the different directions of quantum machine learning and how certain problems could profit from higher-dimensional kernel methods.
- Discuss in which application areas the use of a quantum computer may be sensible or not.
- Implement algorithms for a few example problems that may benefit from quantum computers, taken e.g. from high energy physics or medical imaging.
- 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.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||3.0||Applications of quantum computing||Lorenz, J.||see LSF at LMU Munich||
|UE||1.0||Übungen zu Applications of quantum computing||Lorenz, J.||see LSF at LMU Munich||
Learning and Teaching Methods
• 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.
Description of exams and course work
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.
The exam may be repeated at the end of the semester.