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


We will offer this course again in 2021 Spring semester.

Course information - 2019 Spring

  • Lecturers: András Pályi, Zoltán Zimborás
  • Responsible lecturer: András Pályi
  • Language: English
  • Location: F3212
  • Time: Wednesdays, 12:15-13:45


  • One goal is to provide an introduction to basic concepts of 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 during the course and as homework. We will use the quantum computers of the IBM Quantum Experience project, which are available via the cloud for anyone.
  • Lectures will combine conventional, frontal presentation, and programming exercises. Therefore, the location is a computer lab. Of course, students are welcome to use there own laptop computers.
  • The main resource used for the course is the online documentations of (1) the quantum computers available through the IBM Quantum Experience project [1], and (2) the Qiskit quantum computing framework [2].
  • 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 exercise sheets published in the "Course material" table below. 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 as a zip file.

  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

List of topics

  • Basics: quantum information, python, and qiskit (Lecture 1, AP)
  • Bernstein-Vazirani algorithm (Lecture 2, ZZ)
  • Density matrix. State tomography. Process Tomography. Relaxation. Dephasing. Decoherence. (Lectures 3-5, AP).
  • Quantum algorithms: Deutsch, Grover, Shor, quantum simulation (Lectures 6-9, ZZ)
  • Classical, hybrid, and quantum error correction using the repetition code. (Lectures 10-11, AP)
  • Bell inequalities. Quantum teleportation. (Lecture 12, ZZ)