Course Descriptions for Graduate Certificate in Quantum Computing

MSQC601: Mathematics and Methods of Quantum Computing

This course will provide the student with the necessary mathematical tools and background knowledge to understand, model, and conceptualize quantum computing and its building blocks and systems. We shall review concepts of computation and how they translate to the microscopic world.

MSQC602: Physics of Quantum Devices

An introduction to quantum physics with emphasis on topics at the frontiers of research, and developing understanding through exercises. This course aims to build a bridge between natural principles such as light and atoms and a variety of modern applications. This course will provide the student with the necessary physical intuition and background information on quantum physics so that to be able to understand and appreciate a variety of applications in quantum computing such as quantum currency, encryption, and random number generation.

MSQC603: Principles of Machine Learning

A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders.
The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

MSQC604: Quantum Computing Architectures and Algorithms

Quantum computing aims to utilize quantum properties of matter to efficiently solve problems that classical computing systems would take too long to solve. This course reviews modern quantum computing architectures and algorithms for these platforms. We focus on mapping of optimization and machine learning problems onto Noisy-Intermediate-Scale Quantum (NISQ) architectures and also discuss how to leverage state-of-the-art classical simulation methods for quantum-inspired algorithms.
We review several modern NISQ architectures and associated software interfaces, we analyze performance for optimization and statistical sampling. We survey current literature to review and implement methods for mapping optimization and machine learning problems onto NISQ architectures and modern simulators and use them to solve and study example problems.