EEC189L: Quantum Computing
- CRN: 22425 and 22426, Units: 4
- Professor: Marina Radulaski
- Date/Time: M 2:10 - 3:30 PM & 6:10 - 8:00 PM, W 2:10 - 3:30 PM
- Prerequisites: MAT 22A (may be concurrent), ENG 06, or another programming course
Course Description: Learn the principles of and get hands-on experience with quantum computing! This course is aimed at sophomore and junior students with interest in quantum computing who are familiar with the basics of linear algebra such as vector spaces and matrix manipulations. The course learning goals aim for students to:
• Understand how quantum information is represented and how it differs from classical information,
• Become familiar with the unintuitive concepts of quantum mechanics such as the superposition and entanglement,
• Become familiar with the physical implementations of quantum hardware,
• Learn to design a quantum circuit,
• Learn to program in Qiskit open-source quantum computing software development framework,
• Learn basics of quantum algorithms,
• Apply physical concepts to quantum information hands-on demos,
• Develop interdisciplinary communication and presentation skills.
Graduate EEC 289 Courses
EEC 289A: Introduction to Unsupervised Learning
- CRN: 57374, Units: 4
- Instructor: Yubei Chen
- Prerequisites: ECS 171, or ECS 174, or EEC 174AY, or equivalent; All students need to know python programming (or instructor consent).
- Recommended: Proficiency in linear algebra, calculus or mathematical analysis, applied probability, optimization, and deep learning.
Course Description: Humans and other animals exhibit learning abilities and understanding of the world that are far beyond the capabilities of current AI and machine learning systems. Such capabilities are largely driven by intrinsic objectives without external supervision. Unsupervised learning (also known as self-supervised learning) aims to build models that find patterns in data automatically and reveal the patterns underlying data explicitly with a representation. In the past few years, this subject has not only become one of the pillars for modern machine learning systems, but also provide insights for modeling biological sensory systems. This course covers the historical trajectory (e.g., clustering, spectral embedding, mixture models) and frontier development of unsupervised learning (like deep energy-based models, score-matching based models or diffusion models, and joint-embedding self-supervised learning). The goal is to build the theoretical foundations for the students and help them catch the frontier of this field. Further, students will be required to review selected papers and form groups to present several frontier research topics as mini lectures. Students will also form into groups to work on a final project. This introduction to unsupervised learning will help students significantly improve their ability to conduct fundamental machine learning research or apply the advanced machine learning techniques in their future work.
EEC289K - Electromagnetic Metamaterials and Metasurfaces
- Units: 4
- Instructor: J. Sebastian Gomez-Diaz
- Prerequisite: Fundamentals of Electromagnetics. EEC130AB or equivalent.
- Recommended: EEC230 (Electromagnetics) and EEC235 (Photonics).
- Course Syllabus
Course description: This material is suitable for students specializing in electrical engineering and applied physics – especially electromagnetics, nanophotonics, nanoscience, and radiofrequency. Students will develop a fundamental understanding of light-matter interactions at the nanoscale and learn recent development of the theory and application of metamaterials and metasurfaces. After reviewing the Maxwellian framework, the course will cover the basics of metamaterial concepts and applications such as perfect lenses, epsilon-near-zero responses, and hyperbolic metamaterials, among others. Then, the course focuses on the theory of ultrathin metasurfaces, including homogenization and the Generalized Snell’s law, and their application to manipulate the light in the near and far fields. Beam steering, Faraday rotation, plasmonics and 2D materials are briefly discussed. Finally, selected applications are overviewed, including magnetless nonreciprocal RF circuits and metasurfaces based on time-modulation, nonlinear metasurfaces, leaky-wave antennas, and coupling metasurfaces and MEMS for infrared sensing. Grading will be performed through homework (20%), design project I (30%) and a design project II (50%) – there will be no exams. Students will find and discuss with the instructor specific projects that link their PhD/MSc work with the field of metamaterials and metasurfaces.
EEC 289K - Ultrafast Photonics
- CRN: 22659, Units: 4
- Professor: William Putnam
- MW 4:10-6:00 PM
- Prerequisites: EEC 230 or consent of instructor.
- Course Description: Ultrafast lasers are rapidly finding their way into laboratories all over the world. In this course, we will explore what makes these short-pulse lasers useful for applications ranging from bio-imaging to x-ray generation. Specifically, we will cover the essentials of ultrafast photonics, including the basic science of ultrashort laser pulses, the technology to generate and manipulate these pulses, and a few of the numerous applications of ultrafast photonic systems.
EEC 289Q: Modeling and Optimization for Computer Engineers
- CRN: 22661, Units: 4
- Professor Soheil Ghiasi
- Lecture – 4 hours/week
- Prerequisites: EEC170 and ECS122A (or instructor consent)
- Course Description: The objective of this course to introduce and practice concepts that would enable graduate students working in the broad area of computer engineering to leverage techniques from optimization, particularly discrete optimization, to model and solve problems that may be encountered in their field of research. The course aims to address the gap between core computer engineering classes, such as those focused on design and programming of computing systems, and courses in optimization, which typically focus on foundational treatment of continuous optimization problems.
- Through the course, the students will gain a deeper understanding of both classic and recent techniques used in discrete optimization, and will grow into informed users of optimization tools and methodologies that may find application in their research area. The material will be motivated and discussed in the context of a large number of example problems that arise in different subareas of computer engineering, such as electronic design automation, compilers and operating systems.