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.
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.
For detailed information go to Timetable in Brightspace.
Mode of instruction
Total hours of study: 168 hrs. (= 6 EC)
Lectures: 28:00 hrs.
Tutorials: 20:00 hrs.
Projects and assignments: 120:00 hrs.
Students will be evaluated by means of take-home assignments (50%) and mini projects (50%).
The teacher will inform the students how the inspection of, and follow-up discussion of the exams will take place.
Nielsen and Chuang, Quantum computation and quantum information.
Links to extensive on-line literature to be provided during the course.
Registration for Brightspace occurs via uSis.
How to sign up for classes: