NT409: Quantum Computing and Applications

Introduction

Welcome to NT409

This course gives information technology students an introductory yet practical grounding in quantum computing, taught through programming tools, simulators and cloud quantum platforms. Students study the core concepts of qubits, superposition, entanglement, measurement, quantum gates and circuits, together with the limits of today's NISQ devices. The emphasis is on building, simulating and evaluating simple quantum circuits with frameworks such as Qiskit, Cirq or PennyLane rather than on quantum hardware physics, which is introduced only at the level needed to understand backend constraints. Algorithms including Deutsch-Jozsa, Grover, Shor, QAOA, VQE and introductory Quantum Machine Learning are covered conceptually and through applications, with particular attention to the hybrid classical-quantum workflow in optimisation, networking, cloud computing, resource allocation and security. On completion, students can construct basic circuits, run experiments on a simulator or a cloud backend where access permits, analyse the results, and assess how feasible quantum approaches are for selected IT problems.

Course Information

Course Code

NT409

Credits

3 (2 theory + 1 practice)

Self-Study

90 hours

Prior Courses

Introduction to Computer Networks; Linear Algebra

Learning Outcomes

CLO1: Concepts

Explain qubits, superposition, entanglement, measurement, quantum gates and circuits at a level sufficient for programming and analysis.

CLO2: Algorithms

Describe the main ideas behind Deutsch-Jozsa, Grover, Shor, QAOA, VQE and QML, and the structure of the hybrid classical-quantum workflow.

CLO3: Implementation

Build and run basic circuits with common gates in Qiskit or an equivalent tool, on a simulator or a cloud backend where access permits.

CLO4: Analysis

Analyse how shot count, noise, circuit depth and backend constraints affect results, and judge feasibility for a given application.

CLO5: Teamwork

Work in a team on the labs and the course project, then present and defend the results clearly, including their limitations.

Teaching Levels

Each outcome is taught at one of three levels: Introduce (I), Teach (T) and Use (U).

Instructors

Dr. Dang Van Huynh

Lecturer, Department of Computer Networks

Faculty of Computer Networks and Communications

Course design, quantum optimisation and applications to networking and cloud

danghv@uit.edu.vn

Phan Trung Phat, M.Sc.

Lecturer, Department of Computer Networks

Faculty of Computer Networks and Communications

Course design, quantum programming workflow and laboratory sessions

phatpt@uit.edu.vn

Textbooks

BOOK

Quantum Computation and Quantum Information

Nielsen & Chuang (Cambridge University Press, 2010)
BOOK

Quantum Computing: A Gentle Introduction

Rieffel & Polak (MIT Press)
BOOK

Quantum Machine Learning: A Modern Approach

Karthikeyan et al., eds. (CRC Press, 2024)

Supplementary Material

SLIDE

Lecture slides and practice notebooks

Provided by the instructors throughout the semester
PAPER

Selected research papers

Assigned per topic; students are encouraged to explore open documentation and learning resources

Toolchain

Environment

Python Jupyter / JupyterLab Google Colab VS Code

Quantum Frameworks

Qiskit Cirq PennyLane

Supporting Libraries

numpy matplotlib pandas

Cloud Platforms

IBM Quantum Azure Quantum Amazon Braket

Session Timeline

Scroll to view all sessions

▼ Scroll

Select a Session

Tap a session from the list above to view details here.

Project Lifecycle Guidelines

From team formation (Session 2) to final defence (Sessions 14-15)

Phase 1: Topic Selection

Teams form and choose a small problem in optimisation, networking, cloud, security or introductory QML. Scope matters more than ambition.

Deadline: Session 2

Phase 2: Problem Formulation

Define inputs, outputs, constraints and the objective, then state the evaluation criteria before writing any circuit code.

Deliverable: 1-page outline

Phase 3: Prototype & Progress Review

Present a running notebook with preliminary results, and report progress in class for feedback on tooling and scope.

Checkpoint: Session 10

Phase 4: Experiments & Baseline

Run experiments on a simulator or a cloud backend, vary shots and parameters, and compare with a simple classical baseline where appropriate.

Phase 5: Final Defence

Submit a reproducible notebook and report, then present the results. A clear discussion of NISQ limits counts as much as the results themselves.

Assessment

Topic Distribution

Component	Outcomes	Weight
A1. Attendance, coursework and project	CLO1, CLO2, CLO4, CLO5	30%
A3. Labs (3 sessions)	CLO2, CLO3, CLO5	20%
A4. Final exam	CLO1-CLO4	50%

There is no midterm examination (A2 carries a weight of 0%).

A1 Rubric

Coursework and Project

Attendance at 80% or more of sessions for full marks on the attendance component
Problem definition that is clear and realistically scoped
Appropriate choice of workflow and tools, with justification
Working prototype or notebook with experimental results
Analysis of feasibility and limitations; clear report and defence

A3 Rubric

Labs

Circuits and programs meet the requirements and run reliably
Results are reproducible on a suitable simulator or backend
Analysis correctly explains what each part of the circuit does
Submission is complete, well organised and on time

A4 Rubric

Final Exam

Command of the concepts, algorithms and applications covered
Ability to analyse a problem and apply course material with sound reasoning
Clear, logical and complete presentation of the answer

Course Requirements

Participation and Submissions

Attend regularly and take part in discussion, practice and in-class reporting, all of which count towards assessment
Complete the labs, exercises and project on schedule
Every submission includes experimental results, a description of the procedure and an analysis of the outcome
Reading beyond the lectures, including papers and open documentation, is encouraged

Academic Integrity

Students are responsible for the correctness and reproducibility of their results, including any AI-generated content
Copying content or source code without proper citation is not accepted
Attendance at the final exam is compulsory