Course data
Course name: Quantum Information Processing
Neptun ID: BMETE11MF42
Responsible teacher: András Pályi
Department: Department of Physics
Programme: Courses for Physicist MSc students
Course data sheet: BMETE11MF42
Requirements, Informations

Course information - 2021 Spring semester

  • Lecturers: András Pályi, Zoltán Zimborás
  • Responsible lecturer: András Pályi
  • Language: English
  • Location: Teams (for now)
  • Time: Fridays, 8:30-10:00 - first lecture: Feb 12 Friday.


  • Hands-on quantum computing. One goal is to introduce quantum information theory and computing. Another goal is to provide hands-on experience in programming an actual quantum computer. That is, the basic concepts, gadgets, algorithms, etc., should be implemented and run by the students themselves, partly during the lectures, partly as homework. We will use the quantum computers of the IBM Quantum Experience project, which are available via the cloud for anyone.
  • Lectures. Lectures will combine conventional, frontal presentation, and programming exercises.
  • Assignments. At each lecture, a number of programming exercises are assigned. You will have to submit your solution (pdf version of your jupyter notebook) during the day of the lecture via Teams. These assignments are mandatory, but they do NOT count in your final evaluation (see below).
  • Resources. The main resources used for the course are the online documentations of (1) the quantum computers available through the IBM Quantum Experience project,, and (2) the Qiskit quantum computing framework,
  • Evaluation: There is an exam at the end of the semester. You'll get a few exercises that you have to solve on the spot on your own in a 90-minute time frame. Using online resources is allowed. The exercises will be similar to those on the exercises assigned at the lectures. We suggest that you solve all exercises before the exam as a preparation. You'll get a grade based on the quality and quantity of the solutions you prepare during the exam, and based on your competence revealed at a short discussion after the exercise session.

Course material

Complete course material of 2019 as a zip file. This is just for orientation, major updates in 2021 might follow.

  1. Basics: quantum information, python, qiskit
  2. The Bernstein-Vazirani quantum algorithm
  3. Decoherence 1: the density matrix
  4. Decoherence 2: qubit relaxation
  5. Decoherence 3: quantum state tomography
  6. Deutsch-Jozsa and Grover algorithms
  7. Quantum Fourier Transform, Phase Estimation
  8. Shor's algorithm
  9. Quantum Simulation
  10. Classical Error Correction
  11. Quantum Error Correction
  12. Bell inequalities
  13. Quantum Key Distribution

Further topics

May be covered, time permitting: readout error mitigation, superdense coding, quantum teleportation, etc.