2026 (Current Year) Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Fundamentals of Quantum Information
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Yoshifumi Nakata / Keisuke Yanagisawa
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue (W8E-306) / 7-8 Fri (W8E-306)
- Class
- -
- Course Code
- ART.T469
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 3Q
- Syllabus updated
- Oct 2, 2026
- Language
- English
Syllabus
Course overview and goals
Quantum information science aims to exploit quantum mechanical properties for information processing. By appropriately utilizing quantum phenomena, it becomes possible to perform tasks in computation, communication, and cryptography that are considered infeasible within conventional information-processing frameworks.
This course begins with an introduction of quantum theory from the perspective of information processing and covers fundamental quantum protocols and algorithms. Numerical simulations form an integral part of the course, through which students develop an intuitive understanding of quantum information processing.
More specifically, the course covers the general framework of quantum theory and fundamental concepts, such as superposition and entanglement, and discusses representative quantum protocols and algorithms, including quantum teleportation, quantum key distribution, and Shor’s factoring algorithm. In addition, the course introduces quantum annealing as a computational approach to combinatorial optimization problems. The basics of quantum error correction are also covered.
The course consists of lectures as well as numerical exercises using quantum circuit simulators and quantum annealers.
Course description and aims
By the end of this course, students will be able to:
1. Explain the principles of quantum theory.
2. Describe basic quantum protocols and algorithms that exploit quantum properties.
3. Formulate combinatorial optimization problems for quantum annealing.
4. Explain the basic ideas of quantum error correction and simple examples.
5. Implement programs using quantum circuit simulators and quantum annealers.
Keywords
Quantum information, quantum communication, quantum algorithms, quantum annealing, quantum error correction, quantum circuit simulators
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course consists mainly of lectures, supplemented by numerical exercises using quantum circuit simulators and quantum annealers.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Foundations of Quantum Theory 1 (Nakata) |
Understanding closed quantum systems (pure states, unitary evolution, projective measurement) |
| Class 2 | Foundations of Quantum Theory 2 (Nakata) |
Understanding closed quantum systems and quantum phenomena |
| Class 3 | Foundations of Quantum Theory 3 (Nakata) |
Understanding general framework of quantum systems (mixed states, quantum channels, POVMs) |
| Class 4 | Foundations of Quantum Theory 4 (Nakata&Yanagisawa) |
Conducting numerical simulation using Qiskit |
| Class 5 | Theory of Quantum Information 1 (Nakata) |
Understanding quantum circuit model, quantum correlations, and basic communication protocols |
| Class 6 | Quantum Annealing 1 (Yanagisawa) |
Understanding basics of quantum annealing and quantum Ising model |
| Class 7 | Theory of Quantum Information 2 (Nakata) |
Understanding quantum key distribution and its application |
| Class 8 | Quantum Annealing 2 (Yanagisawa) |
Understanding QUBO formulation and relation to Ising models |
| Class 9 | Theory of Quantum Information 3 (Nakata&Yanagisawa) |
Understanding communication protocols, QKD via numerical simulation |
| Class 10 | Quantum Annealing 3 (Yanagisawa) |
Understanding practical QUBO formulations |
| Class 11 | Theory of Quantum Information 4 (Nakata) |
Understanding quantum algorithms (Shor’s algorithm and Grover’s algorithm) |
| Class 12 | Quantum Annealing 4 (Yanagisawa) |
Conducting QUBO formulation and calculating its solution via Fixstars Amplify |
| Class 13 | Theory of Quantum Information 5 (Nakata) |
Understanding basics of quantum error correction |
| Class 14 | Theory of Quantum Information 6 (Nakata&Yanagisawa) |
Understanding quantum error correction codes with numerical simulations |
Study advice (preparation and review)
Students are expected to spend approximately:
• 30 minutes for preparation before each class
• 120 minutes for review (including assignments) after each class
Students should consult textbooks and distributed materials as appropriate.
Textbook(s)
Course materials will be provided as needed.
Reference books, course materials, etc.
• (Japanese only) S. Ishizaka et al., Introduction to Quantum Information Science (2nd ed.), Kyoritsu, 2024
• (Japanese only) Y. Nakata, Quantum Information Theory, Asakura Publishing, 2024
• (Japanese only) S. Tanaka (ed.), Introduction to Annealing Machines with Python, Kyoritsu, 2025
• Quantum Computation and Quantum Information, Cambridge University Press, 2010
• S. Tanaka et al., Quantum Spin Glasses, Annealing and Computation, Cambridge University Press, 2017
Evaluation methods and criteria
Assessment will be based on short assignments and a final report. The final report will constitute the primary component of the grade, with assignments also taken into account.
Related courses
- CSC.T241 ： Fundamentals of Computing
- CSC.T242 ： Probability Theory and Statistics
- CSC.T271 ： Data Structures and Algorithms
- CSC.T351 ： System Analysis
- CSC.T342 ： Fundamentals of Optimization
Prerequisites
None