CSCI3350 Introduction to Quantum Computing (2026 Spring)

Course Instructor:

• Prof. Xiao LIANG (xiaoliang [at] cuhk.edu.hk)

Time and Venues: Teaching Assistant:

Important Message for Potential Students

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• No programming; all theory: This course is similar in style to CSCI3160 Design and Analysis of Algorithms, focusing on the algorithmic ideas, pseudocode, and theoretical analysis. It does not involve programming, real-world implementation, or software engineering considerations.
• Difficulty level: The course uses linear algebra and probability as foundational tools. You can expect a difficulty level and workload comparable to your undergraduate-level Linear Algebra and Probability Theory courses.
• More than "algorithms": While this course does explore quantum algorithms, it is not solely focused on them. A meaningful understanding of quantum algorithms begins with a solid foundation in the principles of quantum information — including concepts like superposition, entanglement, and measurement — which we will study in depth. The course may also cover several important topics that are not algorithmic in nature but are central to the field of quantum computing, such as quantum error correction, fault-tolerant computation, proofs of quantumness, and non-local games. If you're coming in with the expectation that this course is a direct quantum counterpart to a classical algorithms course like CSCI3160, we encourage you to view it instead as a broader and more foundational exploration of what makes quantum computing unique.
• This course is NOT intended for students who:
  • Want to learn how to build a quantum computer
  • Want to practice quantum programming in a specific (quantum programming) language
  • Expect to learn quantum physics

Grading Scheme

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• Quizzes (16%): There will be two quizzes, each accounting for 8%. The quiz times will be announced at least one week in advance.
• Midterm (40%): There will be one midterm exam. Although it's called a "midterm," it will actually take place in the later weeks of the course (e.g., between Week 8 and Week 15). The exact time of the midterm will be announced at least one week in advance. This scheduling ensures that the exam occurs after most of the course material has been covered. It also compensates for the fact that this course does not have a final exam!
• Group Homework (14%): There will be two problem sets, each worth 7%. Students are allowed to form groups and submit one joint solution. More details will be announced later.
• Group Project (30%): Students will work in groups to choose a topic, read related papers, write a report, and give a presentation. More details will be provided in due course.

Textbooks

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The primary textbook will be Prof. Liang's lecture notes, available in the "all-in-one lecture notes" at the beginning of the Weekly Schedule section below. Here are some supplementary materials you may find helpful (we will add more as the course progresses):

• [NC] – Quantum Computation and Quantum Information (10th Anniversary Edition), by Michael A. Nielsen and Isaac L. Chuang
• [Aar18] – Introduction to Quantum Information Science Lecture Notes, By Scott Aaronson.

Additional Information

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Course Description: This course offers an introduction to the fascinating world of quantum computing, focusing on its fundamental concepts and algorithms. Additionally, as a unique feature, the instructor will guide students through several interdisciplinary areas where quantum computing intersects with fields such as computational complexity, cryptography, machine learning, networking, and information theory. This unique approach aims to equip students with a broad spectrum of quantum-related skills, preparing them to contribute to the rapidly evolving field of quantum computing, which offers abundant opportunities in both industry and academia.

Topics include:

• Basics of quantum information, the linear-algebra formalism
• Entanglement and nonlocality
• The quantum circuit model
• Basic quantum protocols, such as quantum teleportation and superdense coding
• Basic quantum algorithms, such as Simons' algorithm, the Quantum Fourier Transform, Phase Estimation, Shor's Factoring algorithm, Grover search, amplitude amplification
• Quantum error correction and fault-tolerance
• Selective topics from quantum cryptography, proof of quantumness, quantum information theory, quantum complexity theory, quantum machine learning

No background in quantum physics is required. The only prerequisites are familiarity with undergraduate-level linear algebra and probability theory.

Make-up Quiz/Exam Policy: You may apply for a make-up quiz/exam if all of the following conditions are met:

• You must provide a valid justification along with supporting documentation. For example, in the case of illness, a doctor's note clearly stating your condition is required.
• You must email the instructor at least 4 hours before the start of the lecture in which the quiz is administered.

Genuine emergency cases will be handled separately and typically require valid justification along with supporting documentation.

Policy on Regrading Requests: It is not uncommon for students to disagree with a marker's (such as a TA or the course instructor) grading on exams, quizzes, or homework, and to request additional points. If you plan to do so, please read the following policy carefully to make effective use of both your time and that of the marker.

• Regrading will be considered only if points were lost due to a misunderstanding of your written answer by the marker.

In such cases, you are encouraged to clarify your original response so that the marker can reassess it based on what you actually wrote. However, regrading will not be considered if your justification relies on additional reasoning or explanation that was not included in your original answer. The principle is simple: grading must be based solely on what was written during the exam or assignment; there is no fair way to take into account explanations provided after the fact, especially since different students may present different arguments about what they meant to write or how they were thinking during the exam.

Please also note that some questions, such as short-answer, calculation, or proof-based questions, do not have simple yes/no answers and often require multiple steps. It is common for students to argue:

• "I know my answer is incorrect, but it's not 'that wrong.' I included some useful steps, or at least showed that I was on the right track, even though I didn't finish or get the correct final answer. So I believe I deserve partial credit, or more partial credit than what I received."

If you are planning to make this kind of argument, please save your effort. The marker will not adjust your score based on such reasoning. If your answer is incomplete or incorrect, the amount of partial credit is determined entirely by the marker and applied consistently across all students. No special consideration will be given to any individual, either in your favor or against you. Allowing such exceptions would introduce unfairness, as it would depend on the student's ability to argue and the emotional state of the marker, which we aim to avoid.

That said, if you believe your answer was completely correct and that you lost points only because the marker misunderstood your writing, then you are welcome to request a re-evaluation.

Academic Honesty: Students are reminded to carefully review and adhere to the University's policies and regulations on academic honesty, as well as the disciplinary guidelines and procedures governing breaches of these rules. We emphasize the importance of strictly following all examination regulations. During an exam, if an invigilator observes behavior deemed suspicious or potentially indicative of academic dishonesty, they are authorized to issue a warning, record the student's name, and report the incident directly to the Faculty Disciplinary Committee (FDC) for investigation. If the FDC determines that a violation has occurred, the student will receive an automatic failure for the course and may face additional disciplinary actions in accordance with University policy.

All students are strongly encouraged to familiarize themselves with the University's official guidelines on examination conduct and academic integrity. Please refer to the following links:

• Honesty in Academic Work: A Guideline for Students and Teachers
• Guidelines from the Engineering Faculty

In particular, the following sections are relevant to course examinations:

• Rules to be Observed by Candidates at Examination Centre
• CUHK disciplinary guidelines and procedures

Students with Special Educational Needs (SEN): CUHK is committed to promoting equal opportunities in academic pursuits for all students. To support full-time students with special educational needs (SEN) in fully participating in campus life and enhancing their learning experience, the SEN Service (SENS) provides tailored support based on individual needs. These may include learning aids and equipment, special arrangements for classes or examinations, accessible facilities, and assistance with hostel visits and accommodations.

Students who require these services should first register with the Office of Student Affairs (OSA) and undergo an assessment by the SEN team. If a student is identified as needing SEN support, the recommended academic accommodations will be communicated to the SEN coordinator of the relevant teaching unit, who will then inform the course instructor accordingly. If you have completed the registration and assessment process with the SEN team at OSA, please email the course instructor to discuss arrangements that best accommodate your needs.

Use of AI tools: This course follows "Approach 2 – Use only with prior permission" according to the University's policy on the use of AI tools. In particular, the use of AI tools is prohibited in all assessment-related components (including quizzes, the midterm, and the final exam). However, students are permitted to use AI tools for non-assessed learning activities, such as practicing exercise problems that do not count toward the final grade.