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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 most recent timetable can be found on the students' website.

Mode of instruction

  • Lectures

  • Tutorials

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 homework assignments (50%) and mini projects (50%).


See Blackboard.

Reading list

  • Nielsen and Chuang, Quantum computation and quantum information.

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


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

  • Please also register for the course in Blackboard.


Lecturers: dr. V. Dunjko & T.E. O'Brien Msc