A lap 2019. május 22., 11:15-kori változata

Tartalomjegyzék

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
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	ExercisesLecture12.pdf	SolutionLecture11-Exercise2.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)

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 | [[Media:QIP2019Spring-Lecture12.pdf|Bell inequalities]]
-| [[Media:ExercisesLecture11.pdf|ExercisesLecture11.pdf]]
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-| [[Media:SolutionLecture11-Exercise2.pdf|SolutionLecture11-Exercise2.pdf]] [[Media:SolutionLecture11-Exercise3.pdf|SolutionLecture11-Exercise3.pdf]]
+| [[Media:SolutionLecture11-Exercise2.pdf|SolutionLecture11-Exercise2.pdf]]
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