Quantum Information and Complexity

Last Updated: Fall 2024

1. Credits and contact hours: 3 credits
2. Instructor’s or course coordinator’s name: Liyi Li
3. Text book, title, author, and year: None required
4. Other supplemental materials: None

Specific course information

1. Brief description of the content of the course: A computer is a physical device that helps us process information by executing algorithms. An algorithm is a well-defined procedure, with finite description, for realizing an information-processing task. An information-processing task can always be translated into a physical task. When designing complex algorithms and protocols for various information-processing tasks, it is very helpful, perhaps essential, to work with some idealized computing model. However, when studying the true limitations of a computing device, especially for some practical reason, it is important not to forget the relationship between computing and physics. Real computing devices are embodied in a larger and often richer physical reality than is represented by the idealized computing model. Quantum information processing is the result of using the physical reality that quantum theory tells us about for the purposes of performing tasks that were previously thought impossible or infeasible. Devices that perform quantum information processing are known as quantum computers. In this course, we examine how quantum computers can be used to solve certain problems more efficiently than can be done with classical computers.
2. Prerequisites or co-requisites: n/a
3. Required, elective, or selected elective? Selected Elective

Specific goals for the course

Upon completing this course, students will be able to do the following:

• Different representations of quantum states and programs in the context of computer science. We will discuss different programming languages and computing techniques to understand quantum computing behaviors.
• Different quantum computation models, such as quantum circuit-based, Hamiltonian simulation models, and particle system models. After the course, students can utilize these different models to develop quantum programs.
• Different quantum algorithms are used to solve different problems. After the course, students can write quantum programs based on different classes of quantum algorithms to solve classically difficult problems, such as particle system simulation.