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Applied Quantum Algorithms


Admission requirements

Not applicable.


Quantum computing is an upcoming technology which is expected to have game-changing applications in all computationally-intensive branches of the natural sciences; material sciences, chemistry and physics. Resource-efficient algorithm design is an essential step towards making quantum computers viable for these applications. This is an advanced course for students with understanding of the basics of quantum computing (e.g. for students which have taken Quantum Algorithms at LIACS or Quantum Information at LION), that emphasises practical quantum computing for potential near-term applications. In this course, you will learn about modern quantum algorithmic techniques, and how they are applied in quantum chemistry, quantum many-body physics and machine learning. Furthermore, you will learn about the fundamentals of quantum error correction and error mitigation techniques required to make noisy quantum computers functional.

Course objectives

The students will:

  • be able to interpret, evaluate, design and optimize quantum circuits;

  • be able to explain how variational quantum eigensolver and quantum phase estimation works;

  • know how to apply these quantum algorithms to problems in quantum chemistry and many-body physics;

  • know how to apply these algorithms to problems in machine learning;

  • be able to describe noise, quantum error correction and error mitigation.

The algorithms will also be implemented in a python-based language for quantum computing.


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For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

See Brightspace.

Course load

Total hours of study: 168 hrs. (= 6 EC)
Lectures: 28:00 hrs.
Tutorials: 20:00 hrs.
Projects and assignments: 120:00 hrs.

Assessment method

Students will be evaluated by means of two take-home assignments. The average score of the two-take home assignments carry 50% of the final grade, and a final individual mini-project (carries 50% of the final grade). 50% is the smallest passing grade.
In formulas: THA1, THA2 and MP denote the score for the first, second take home assignment and for the miniproject, respectively. Each score is between 0 and 100.
The final score is FIN = (THA1+THA2)/4 + MP/2, and 50 is the smallest passing passing.

The teacher will inform the students how the inspection of, and follow-up discussion of the exams will take place.

Reading list

  • Nielsen and Chuang, Quantum computation and quantum information.

  • Links to extensive on-line literature to be provided during the course.


From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


Lecturers: dr. V. Dunjko, dr. J. Tura Brugués