194.137 Quanten Computing 2
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VU, 4.0h, 6.0EC, to be held in blocked form


  • Semester hours: 4.0
  • Credits: 6.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to understand mixed states, density operators and their applications. Central algorithms such as QPE and QFT are clear at the circuit level. State preparation and oracle substitution can be applied. Transpilation and the effect on depth of algorithms are recognized. The meaning of read errors is clear. Measurement of arbitrary observables over Pauli strings is explained algorithmically. HHL is conceptually understood. The structure of hybrid variational algorithms is clear, as is the influence of barren plateaus. The treatment of categorical data, dimension reduction, and the structure of neural networks is understood. The structure of exemplary quantum neural networks is clear. Clustering, classification, kernel methods and their possibility of realization using quantum computers is understood - and thus the potential of quantum machine learning is seen. Furthermore, selected topics of the "Fundamentals of Quantum Informatics" are deepened.

Subject of course

  • Mixed states, density operators, reduced density operators
  • Quantum Information
  • QFT, QPE, Amplitude Amplification
  • State Preparation & Oracle Replacement
  • Transpilation, Connectivity, Readout Errors
  • Hamilton Simulation, HHL Algorithm, Quantum Linear Algebra
  • Pauli strings, measurement of arbitrary observables
  • Variational Algorithms, VQE, QAOA, Warm-Starting, Barren Plateaus, Quantum Gradients
  • Categorical data, dimension reduction, quantum neural networks, quantum no-free lunch
  • support vector machines, quantum kernels
  • Hybrid algorithms, quantum software engineering

Teaching methods

Using what learned with QisKit und IBM Q.

1.3., 14 - 15 Uhr: WebEx
17.3, 22.3., 28.3., 10 - 13 Uhr: on site

Inverted Classromm - Watch videos by 10.3.2023, discussion afterwards at 17.3., 22.3., 28.3., on site from 13:00h.

During the exercises, the contents of the lecture will be practiced and consolidated by means of practical examples.

Mode of examination


Additional information

Kickoff:  22. Februar 2023, 11:00 Uhr, auf WebEx - Link:

Videos der Vorlesung, die Handouts sowie die Übungsblätter werden Online zu Verfügung gestellt.
Die Übungen selber als auch eine Frage- bzw. Diskussionsveranstaltung wird persönlich stattfinden. 

Barzen & Leymann (Vorlesung), Mandl (Übungen)

ECTS Breakdown :
6 ECTS entsprechen 150 Stunden

20 Stunden Vorlesung, 40 Stunden Vorlesungsvorbereitung, 15 Stunden Wiederholung für die Prüfung und
75 Stunden Übung

Prof. Frank Leymann (Univ. Stuttgart) - frank.leymann@iaas.uni-stuttgart.de
Dr. Johanna Barzen   (Univ. Stuttgart) - johanna.barzen@iaas.uni-stuttgart.de
Dipl.-Ing. Alexander Mandl (Univ. Stuttgart) -  alexander.mandl@iaas.uni-stuttgart.de



Course dates

Wed11:00 - 11:3022.02.2023 via Webex (LIVE)Kickoff
Wed14:00 - 15:0001.03.2023 via Webex (LIVE)Übung
Fri10:00 - 15:0017.03.2023Seminarraum 384 Übung
Wed10:00 - 14:0022.03.2023Seminarraum 384 Übung
Tue10:00 - 14:0028.03.2023Seminarraum 384 Übung
Course is held blocked

Examination modalities

Exercises and oral exam about lecture.

Course registration

Begin End Deregistration end
13.02.2023 08:00 05.03.2023 23:59 05.03.2023 23:59


Study CodeObligationSemesterPrecon.Info
ALG For all Students Elective


No lecture notes are available.

Previous knowledge


  • Attendance Required!