Undergraduate (3rd and 4th years)
This is information from a past (2019) course.
The URLs, account and classroom informations have been removed to prevent the leakage of internal information for online classes.
Last updated at Oct 17, 2025.
Class plans and classrooms are subject to change, so be sure to check UTAS for the latest information.
If you do not have access to the UTAS, please contact your instructor or academic affairs office.
Last updated at Oct 17, 2025.
Class plans and classrooms are subject to change, so be sure to check UTAS for the latest information.
If you do not have access to the UTAS, please contact your instructor or academic affairs office.
Special Lectures in Information Science II
Quantum Computing
This course gives introduction to the theory and applications of quantum computing. The course covers broad topics ranging from the basics of quantum mechanics and quantum algorithms (e.g., the Shor’s prime factorization and the Grover’s search) to recent advances of algorithms for noisy intermediate-scale quantum devices as well as their possible applications in machine learning and optimization. The course also introduces how to implement quantum algorithms using open-source framework of quantum computing.
Code
Course title
Lecturer
Semester
Period
0510062
FSC-IS4062L1
Special Lectures in Information Science II
Fri 5th
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Language
English
Credit
2
Lecturers with practical experience
YES
Other Faculty
NO
Course Offered by
Science
Schedule
1. Introduction and Outline
2. Basics of Quantum Mechanics for Quantum Computing
3. Quantum Finite Automata
4. Quantum Bits, Gates, and Circuits
5. Programming Quantum Bits
6. Quantum Teleportation, Superdense Coding, and Random Access Coding
7. Quantum Algorithms: Deutsch-Jozsa and Bernstein-Vazirani
8. Quantum Algorithms: Grover Search and Applications
9. Quantum Algorithms: Simon and Shor
10. Programming Quantum Algorithms
11. Quantum Query and Communication Complexity
12. Noisy Intermediate-Scale Quantum Devices
13. Quantum Error Correction and Error Mitigation
14. Quantum Machine Learning and Optimization
15. Programming Noisy Intermediate-Scale Quantum Devices
Teaching Methods
Weekly lectures with exercises, class discussion (one per semester), and group work (one per semester) for working on group projects and final reports.
Method of Evaluation
Examination and homework (70%), group work (15%), and final report (15%). The submission of the final report is mandatory.
Required Textbook
Nielsen, Michael A., and Isaac L. Chuang. Quantum Computation and Quantum Information. Cambridge, UK: Cambridge University Press, September 2000. ISBN: 9780521635035.
Reference Books
Preskill, J. Notes on Quantum Computation. (http://theory.caltech.edu/people/preskill/ph229/ )
R. de Wolf. Quantum Computing: Lecture Notes. (http://homepages.cwi.nl/~rdewolf/qcnotes.pdf )
Notes on Taking the Course
Prior knowledge of linear algebra, classical algorithms, and programming are required. Understanding quantum mechanics is a plus but not required. Familiarity with programming tools is a plus because some homework and group work require students to write programs.
Relationship between practical experience and course content
IBMにおいて取り組んできた量子コンピューティングにおける研究と実システムの利用展開の実務経験を活かして、量子計算論について講義する。
