Quantum information processing exploits the laws of quantum mechanics for computational and communication tasks, and outperforms its classical counterparts. The course is designed to graduate students to introduce quantum information processing. It begins with fundamental principles of quantum information processing and deals with efficient quantum algorithms and communication protocols.

Quantum computing is a new class of the computing technology that utilizes the principles of quantum physics to represent and process information. This course teaches the fundamental understandings of how quantum computing can outperform digital computing by reviewing the basic principles of quantum computing and its algorithms, and discusses about the practical quantum computing models and their applications.

This course covers fundamental VLSI device physics for graduate students. After a brief review of basic quantum mechanics and semiconductor processes, the lecturer will cover basic principles of operation in semiconductor devices including PN junction, MOS Capacitor, MOSFET and bipolar transistors with a strong emphasis on deep submicron secondary effects of MOSFET and bipolar transistors for extensive understanding of advanced device engineering. (Prerequisite: EE362)

This course primarily emphasizes "quantum mechanics" and "statistical physics" for engineers. Quantum mechanics includes a history of quantum physics, Schrödinger equation, a concept of a wavepacket, and N-degrees of freedom. Statistical physics covers a motivation, concept of ensemble average, Boltzmann distribution, Bose-Einstein distribution, Fermi-Dirac distribution, and Non-Equilibrium statistics.