| Seminar | Monday 10:30 - 11:50 | GD110 | |
| Seminar | Monday 10:30 - 11:50 | GD110 | |
Office hours for Fall 2026 are by appointment. Send me email to book a Teams call or in-person meeting.
For times when I might be available, look for blank spots in my public schedule
This course introduces the mathematical and complexity theoretic foundations of quantum computing. Roughly speaking quantum information is the study of information encoded in the state of quantum physical systems.
We will start by studying the simplified formulation of quantum information in terms of tensor products of complex vectors. We'll consider the states of single and multiple systems, and introduce matrix descriptions of quantum circuits.
Having learned the basics of quantum circuits, we will study how they can actually be used as a model of computation. We will cover the notion of quantum advantage motivating quantum computing. We'll start with quantum query algorithms, then move on to more practically interesting problems like integer factorization and unstructured search.
We will conclude by studying a second general formulation of quantum information. This models quantum states as density matrices, and state changes as channels. It has the crucial advantages of being able to model unvertainty and noise. We'll also discuss measures of similarity and distance for quantum states.
Time permitting, we will introduce the idea of quantum error correction.
We will use the text Understanding Quantum Information and Computation by John Watrous.
We will meet once a week to discuss one lesson from the Watrous text. Each meeting will start with a short (20-30 minutes) quiz. Following the quiz, one student will lead a discussion on the current lesson.
There will be about 10 quizzes, for a total of 50% of the marks.
25% of the marks will be for presentation(s) and leading discussions.
25% of the marks will be for a project. Options for the project include a survey paper on some relevant area or a programming project using qiskit or a similar simulator environment.
The student is expect to have a solid grasp of linear algebra, some probability theory, and a reasonable background in algorithms, including asymptotic notation.