2020 Faculty Courses School of Science Department of Physics Graduate major in Physics
Quantum Information
- Academic unit or major
- Graduate major in Physics
- Instructor(s)
- Todd Tilma
- Class Format
- Lecture (Zoom)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Tue (Zoom) / 1-2 Thu (Zoom)
- Class
- -
- Course Code
- PHY.Q435
- Number of credits
- 200
- Course offered
- 2020
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
This is a six-part graduate-level introduction to quantum information and its associated technologies. One third of the class is devoted to discussions on the theoretical underpinnings of quantum information; one-third to discussions on the various physical realizations of said theory; and one-third to experimental work on the IBM Q Experience. At the end of this course, students will have a general understanding of the current state-of-the-art of quantum information science that any theorist, or experimentalist wanting to be in the field of quantum information technology, should know.
Course description and aims
By the end of this class students are expected to be able to understand and make use of the differences between classical and quantum information theory; understand the various physical systems used for quantum information technologies and their limitations; be able to design and implement various quantum circuits on both simulators and real processors; and be able to exploit available and forthcoming quantum information technology products for their own research.
Keywords
Quantum Information Science, Quantum Computer (Theory), Quantum Computer (Experiment), Quantum Algorithms, Quantum Error Correction, Quantum Simulation
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This class is divided into six, three-week parts; each part focused on understanding various key aspects of quantum information science and technology. By the end of the class, students should be able to understand the basic science and engineering concepts behind current quantum information technologies.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to quantum mechanics and the foundations of classical computing - Theory | Physical basis of information; Waves and interference; Quantum superposition and entanglement; etc. |
| Class 2 | Introduction to quantum mechanics and the foundations of classical computing - Applications | Classical vs. quantum bits; Classical vs. quantum computational complexity: etc. |
| Class 3 | Introduction to QISkit and the IBM Q Experience | Installation and overview of Python software development kit (SDK) for working with OpenQASM and the IBM Q Experience (QX); Jupyter notebooks and Anaconda integration; etc. |
| Class 4 | Introduction to quantum information and the foundations of quantum computing - Theory | Entanglement; The quantum Fourier transform and periodicity; etc. |
| Class 5 | Introduction to quantum information and the foundations of quantum computing - Application | DiVincenzo criteria; Quantum gates; etc. |
| Class 6 | Basics of quantum gates on the IBM Q Experience (QX) | Deployment of simple gates on the IBM Q Experience; etc. |
| Class 7 | Introduction to simple quantum protocols and quantum algorithms - Theory | Integer factorization; Phase estimation; etc. |
| Class 8 | Introduction to simple quantum protocols and quantum algorithms - Application | Deutsch–Jozsa algorithm; Shor’s algorithm; Grover’s algorithm; etc. |
| Class 9 | Basics of quantum algorithms on the IBM Q Experience (QX) - Part I | Deployment of simple one and two-qubit algorithms on the IBM Q Experience; etc. |
| Class 10 | Introduction to Noisy Intermediate-Scale Quantum Computing (NISQ) - Theory | Hamiltonian simulation; Quantum walks; etc. |
| Class 11 | Introduction to Noisy Intermediate-Scale Quantum Computing (NISQ) - Application | Quantum chemistry; Machine learning; etc. |
| Class 12 | Basics of quantum algorithms on the IBM Q Experience (QX) - Part II | Deployment of simple multi-qubit algorithms on the IBM Q Experience; etc. |
| Class 13 | Introduction to noise and error correction - Theory | Noise and the framework of quantum channels; Quantum error correction codes; etc. |
| Class 14 | Introduction to noise and error correction - Application | Physical phenomena as quantum bits; Large-scale quantum error correction; etc. |
| Class 15 | General quantum algorithms on the IBM Q Experience (QX) - Part I | Deployment of general multi-qubit algorithms on the IBM Q Experience; etc. |
| Class 16 | Introduction to quantum communication and other quantum information technologies - Future Technologies | Quantum state discrimination and tomography; High-level quantum programming; etc. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
N. David Mermin, “Quantum Computer Science - An Introduction”
https://doi.org/10.1017/CBO9780511813870
Reference books, course materials, etc.
M. A. Nielsen and I. L. Chuang, "Quantum Computation and Quantum Information"
ISBN : 978-1107002173
As needed, appropriate course materials and references will be made available before class via Moodle (https://tilma-labs.org/moodle) and Slack (https://tt-physics-qit.slack.com/).
Evaluation methods and criteria
Assessment is thru attendance, homework, and the successful execution of assigned simulations and experiments on the IBM Q Experience
Related courses
- ZUB.Q204 ： Quantum Mechanics I
- ZUB.Q313 ： Quantum Mechanics III
- MCS.T204 ： Introduction to Computer Science
- ICT.C601 ： Quantum Information Processing
Prerequisites
There are no prerequisites.
Other
Before coming to class, students should read the course schedule and check what topics will be covered. Required learning should be completed outside of the classroom for preparation and review purposes.