COE 531 Advanced Quantum Computing and Communication Fall 2026 (Term 261) · Computer Engineering Department
Course Information
- Lectures
- Monday & Wednesday, 8:10–9:25 PM
- Office hours
- Monday & Wednesday, 9:30–10:30 PM on Teams (or message me on Teams)
- Prerequisites
- Graduate standing
- Textbook
- Quantum Computing and Information: A Scaffolding Approach
- Syllabus
- Download (PDF)
Description
Combinatorial optimization. Quantum optimization. Variational quantum algorithms. Quantum noise models. Quantum error-correction. Quantum fault-tolerance. Quantum communication protocols. Quantum key exchange: BB84. Quantum Internet.
Objectives
- Expose the students to advanced quantum algorithms in the NISQ era.
- Introduce the students to theoretical and practical knowledge for building quantum communication protocols.
Learning Outcomes
After taking this course, students will have the ability to:
- Explain the underlying principles of quantum combinatorial optimization problems, and discuss the potential applications.
- Understand the correct type of quantum algorithm for each problem.
- Design and analyze variational quantum algorithms.
- Identify the challenges of transferring quantum information.
- Implement and utilize quantum error-correction codes to enhance the reliability of quantum computing systems.
Evaluation
| Short Quizzes | 15% |
| Programming Assignments (2) | 20% |
| Lab Experiment | 10% |
| Term Project | 25% |
| Final Exam | 30% |
Suggested Projects
Optimization problems
- Radio antenna placement for network coverage optimization
- Autonomous mission routing for high-risk environments
- Optimized spectrum and frequency assignment
- Optimized satellite tasking and mission scheduling
- Network resilience analysis and critical node identification
Partial differential equations
- Heat equation
- Poisson equation
- Navier–Stokes
- Fisher–KPP
- SIR model
Lectures
Combinatorial Optimization
| # | Lecture | Slides |
|---|---|---|
| 1 | Combinatorial Optimization: optimization problems and the combinatorial family | |
| 2 | Modelling Combinatorial Optimization Problems: instances, feasible solutions and cost functions | |
| 3 | Solving Combinatorial Optimization Problems: greedy, dynamic programming, approximation, and the Ising/QUBO form | |
| 4 | Hardness, Equivalence, and Quantum Annealing |
Variational Quantum Algorithms
| # | Lecture | Slides |
|---|---|---|
| 5 | The Variational Quantum Algorithm Framework | |
| 6 | The Variational Quantum Eigensolver (VQE) | |
| 7 | The Quantum Approximate Optimization Algorithm (QAOA) | |
| 8 | Partial Differential Equations: how they are solved classically | |
| 9 | Solving PDEs on a Quantum Computer: HHL, the variational linear solver, and H-DES |
Quantum Error Correction and Fault Tolerance
| # | Lecture | Slides |
|---|---|---|
| 10 | Quantum Error Correction: why we need it | |
| 11 | Noise Models | |
| 12 | The Repetition Code: from majority vote to Shor's nine-qubit code | |
| 13 | The Language of Quantum Codes: distance, rate, correctability | |
| 14 | The Stabilizer Formalism | |
| 15 | CSS Codes | |
| 16 | From the Surface Code to qLDPC Codes |
Quantum Communication
| # | Lecture | Slides |
|---|---|---|
| 19 | BB84: turning no-cloning into a key exchange |