CMSC 657 Introduction to Quantum Information Processing, Fall 2019
Course Description
Quantum computers have the potential to efficiently solve problems that are intractable for classical computers. This course will explore the foundation of quantum computing. As this is a multidisciplinary subject, the course will cover basic concepts in theoretical computer science and physics in addition to introducing core quantum computing topics. No previous background in quantum mechanics is required. Strong background in linear algebra is recommended!
Tentative topics include: quantum mechanics of qubits; quantum entanglement; quantum protocols; quantum circuits and universality; simple quantum algorithms; quantum Fourier transform; Shor factoring algorithm; Grover search algorithm; quantum complexity theory; selected additional topics as time permits.
Quantum Computers Animated!
Previous offering of the course
CMSC 657 in Fall 2018 for examples of lectures, homework, and projects.
New Lecture Notes from this course
Course Project Showcase
Automatic Decomposition of Quantum Circuits onto Small Quantum Machines by Benjamin Black, Onur Kulaksizoglu, Lillian Huang.
Survey on the Security of the Quantum ROM by Erica Blum, Makana Castillo-Martin, Michael Rosenberg.
A Survey of Quantum Control Architecture by Alexander Brassel, Jason Kuo, Jacob Young.
A Survey on Digital Signatures in Quantum Setting by William Ling Chen, Kamil Doruk Gur, Wenqing Xu.
Quantum Support Vector Machines by Hong-Min Chu, Dan Hofman, Guanwen Yan.
On QMA and Related Complexity Classes by Sharmila Duppala, Mackenzie Kong-Sivert, Juan Luque.
Expressive Power of Parametrized Quantum Circuits by Jingling Li, Qingyang Tan, Xuchen You.
Verifications of Quantum Programs: from ZX-Calculus to more by Yiyun Liu, Yuxiang Peng, James Parker.
Quantum Random Access Memory by Aaron Green, Emily Kaplitz.
Comparison of Unitary Coupled Cluster Ansatz Methods for the Variational Quantum Eigensolver by Ethan Hickman, Aaron M. Roth, Yingyue Zhu.
Quantum Games and Entanglement as a Resource by Dhruv Devulapalli, Joey Brock.
Generics
Prerequisite: Familiarity with complex numbers and basic concepts in linear algebra (e.g., eigenvalues, eigenvectors, Hermitian and unitary matrices).
Lectures: CMSC 657, Tu Th 12:30pm – 1:45pm, CSI 1121.
Instructor: Prof. Xiaodi Wu
Office: IRB 5210 Email: xwu (at) cs.umd.eduTeaching Assistant: Jessica Thompson Email: jktho@cs.umd.edu
Syllabus: check here
Office hours:
Wu: Tu 3:00pm – 4:00pm or by appointments, at IRB 5210.
Thompson: M W 1:30 pm – 3 pm. Location: east side on the 5th floor of IRB.
Evaluation: class participation (3%), assignments (46%), exam (26%), and project (25%). Details in the policy page.
How to Navigate through the Course
Quantum information and computation is an exciting emerging field. It is simply impossible to cover the all relevant topics, especially in an introductory course. Thus, the main goals of this course are
(1) understand and comprehend the theoretical foundation of quantum information and computation. It might not be the case you can understand all research papers after this course. However, it is expected that you can understand the basic language, and can find relevant references for the parts that you don't understand. Thus, one should be able to read research papers and learn more materials in the future.
(2) cover a selective collection of fundamental topics in quantum algorithms And quantum complexity. It is expected that you will know certain important concepts in these fields and can reason about them in both the high level and in sufficient details.
(3) learn about the research frontier of one specific topic via the course project. It is expected to be a valuable experience of reading research papers and making use of the knowledge from (1), (2), especially for graduate students.
The study of this course consists of a large amount of reading materials. Given the difficulty of the materials, a significant amount of effort is expected.
Please treat the course project as a training of your ability to navigate and collect information from literature and to efficiently understand the main points of research papers. It would be wonderful if something original comes out in the project. However, it is perfectly fine if it doesn't. The main purpose of the course project is to facilitate your research in the future.
Assignments
Homework assignments must be submitted electronically to ELMS. (Anyone having trouble with electronic submissions should contact the instructor as soon as possible.) I highly recommend the use of 
for the typesetting.
In particular, we will reward the use of Latex by bonus points (extra 5% of your points).
Here is a good reference about the use of . Here is a latex template for writing solutions. Check the homework page.
Textbooks & Lectures
We will mainly refer to notes (available online or our own) for lectures. We will also refer to parts of the following textbooks for further references.
Paul Kaye, Raymond Laflamme, and Michele Mosca, An Introduction to Quantum Computing, Oxford University Press (2007).
M. Nielsen and I. Chuang. Quantum Computation and Quantum Information, Cambridge University Press; 10 Anv edition, 2011.
A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi. Classical and Quantum Computation (Graduate Studies in Mathematics), AMS, 2002.
John Watrous. The Theory of Quantum Information, Cambridge University Press, 2018.
We also maintain a collection of additional resources at the mini-library page.
Social Media
We use Piazza as the discussion forum. Piazza is FERPA-compliant in that it protects the privacy of students, keeps the information private, and is not searchable by search engines. In order to participate, all students are expected to register with an email address of their choice.
We use ELMS for submissions of assignments and projects and distributions of corresponding grades.
This website serves as the collection of information about the course, syllabus, handouts, and references. Please check frequently!
Web Accessibility
Please refer to UMD Web Accessibility.