Special Topics in Quantum Information Science (EE5105, Fall 2025)

Prerequisites

Students enrolling in this course are expected to have a solid understanding of linear algebra and probability, equivalent to the content covered in EE1002 (Engineering Mathematics - Linear Algebra) and EE2007 (Engineering Mathematics - Probability and Statistics).

Additionally, familiarity with basic concepts of quantum information science, as taught in CommE5061 (Quantum Information and Computation) or in Phys8049 (Introduction to Quantum Computation and Information), is recommended for better comprehension of the material.

Course Staffs

Topics

This is an advanced course intended for students interested in research in quantum information science. We plan to cover various research topics in quantum computing, with emphasis on quantum algorithms, including:

• Mathematical preliminaries
• Quantum algorithms for algebraic problems
• Quantum walk algorithms
• Quantum query complexity and lower bounds
• Hamiltonian simulation algorithms
• Quantum singular value transform
• Clifford+T synthesis
• Learning properties of quantum states

References

There is no required textbook. Good references for background materials include

• Quantum Computation and Quantum Information by Nielsen and Chuang
• The Theory of Quantum Information by Watrous
• Classical and Quantum Computation by Kitaev, Shen, and Vyalyi
• An Introduction to Quantum Computing by Kaye, Laflamme, and Mosca

These lecture notes will often be consulted:

• Lecture Notes on Quantum Algorithms by Andrew Childs
• Lecture Notes on Quantum Algorithms for Scientific Computation by Lin Lin
• Quantum Computation and Quantum Information by Ryan O’Donnell
• Quantum Computing: Lecture Notes by Ronald de Wolf

References for each topic covered in this course will be listed along with the schedule below.

Evaluation

• Assignments (55%)
• Exam (20%)
• Project (20%)
• Scribe (5%)

Assignments

The course includes five written homework assignments, each contributing 10% to the final grade. Additionally, an “Assignment 0” (worth 5% of the total grade) will be distributed during the first class to help students assess whether this course is suitable for them. All assignments must be typeset using LaTeX. An online editor, such as Overleaf, may be useful if you prefer not to set up a LaTeX toolchain yourself. The assignments will be made available and should be submitted using NTU COOL.

Late Submission Policy:

• Submissions within 72 hours: 20% penalty.
• Submissions within one week: 40% penalty.
• Submissions after one week will not be accepted.
• No late submission is accepted for Assignement 0.

Policy on Using AI Tools: If you use an AI tool, you must disclose the name of the tool and how you use it in your submission. Any use of AI tools must align with the policy that all submissions must be based on your own understanding.

Scribe

Students are required to form groups of up to two members to prepare a lecture note for one class session. A template is available here. By the end of Week 3, please inform the TAs which lecture your group will be responsible for scribing. The completed lecture note must be submitted within two weeks after the corresponding lecture. All lecture notes will be posted on NTU COOL for registered students to access.

Project

Students are required to form groups of up to two members for the final project. Project presentations will take place during the last few weeks of the semester. Each group must submit a written project report (typeset in LaTeX) after the last class meeting.

The project consists of the following three parts:

• A project proposal (due October 22), worth 20% of your project grade
• A presentation on December 10 (40%)
• A final paper (40%, due December 24)

Each group should email the instructor to schedule a meeting before the proposal deadline.

A list of possible topics can be found here.

Exam

An exam will be given in the week of final exams.

Schedule (Tentative)

† An: Assignment #n, PP: project proposal, FP: final paper