Tartalomjegyzék

Quantum Information Processing

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.

#	Lecture	Exercises	Solutions	Homework Solutions
01	Basics: quantum information, python, qiskit	ExercisesLecture01.pdf	SolutionsLecture01.pdf	SolutionsLecture01-QX.pdf
02	The Bernstein-Vazirani quantum algorithm
03	Decoherence 1: the density matrix	ExercisesLecture03.pdf	SolutionsLecture03.pdf
04	Decoherence 2: qubit relaxation	ExercisesLecture04.pdf Q0_test.txt	SolutionsLecture04.pdf SolutionLecture04-Exercise4.pdf
04	Decoherence 3: quantum state tomography	ExercisesLecture05.pdf	SolutionsLecture04.pdf SolutionLecture04-Exercise4.pdf

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)