Kvantuminformatika

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Quantum Information Processing

Course Information, 2019 Spring

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

Details

  • 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

# 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

05 Decoherence 3: quantum state tomography ExercisesLecture05.pdf SolutionLecture05-Exercise2.pdf

SolutionLecture05-Exercise3.pdf tomography_test.txt

06 Deutsch-Jozsa and Grover algorithms ExerciseLecture06.pdf

ExerciseSolutions06-DJ.pdf ExerciseSolutions06-Grover.pdf

07 Quantum Fourier Transform, Phase Estimation

QFT_Ex1_v3.pdf

08 Shor's algorithm ExerciseLecture08.pdf Shor.pdf
09 Quantum Simulation ExerciseLecture09.pdf
10 Classical Error Correction ExercisesLecture10.pdf SolutionsLecture10.pdf
11 Quantum Error Correction ExercisesLecture11.pdf SolutionLecture11-Exercise2.pdf SolutionLecture11-Exercise3.pdf
12 Bell inequalities ExerciseLecture12.pdf SolutionLecture12.pdf

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)