Course Descriptions for the Master of Science in Quantum Computing

Core Courses

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.

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.

MSQC605: Advanced Quantum Computing and Applications

When Richard Feynman first introduced the concept of quantum computers it was posed for the purpose of simulating nature. Today quantum simulation remains one of the likely first applications to benefit from quantum computers. This course introduces key concepts required for quantum simulation, and builds tools for performing quantum simulation using state-of-the-art architectures.
We introduce classical schemes, like tensor networks, and machine learning approaches, that can be used for these simulations on CPU/GPU architecture. We survey current literature to review and implement methods of quantum simulation and use them to solve and study example problems.

MSQC606: NISQ Algorithms

Prerequisite: MSQC601 and MSQC602.

Quantum computation is a rapidly growing field at the intersection of physics and computer science, electrical engineering and applied math. While instrumentation of quantum computers is in its infancy, quantum algorithms are being developed to provide efficient solutions to various computational problems.

This course covers basic quantum computing, including quantum circuits, significant quantum algorithms, and hybrid quantum-classical algorithms, with focus on applying the concepts to programming existing and near-future quantum computers. Example codes, homework assignments, and class projects will employ Python modules to handle the data exchange with quantum computers.

MSQC607: Advanced Topics in Quantum Computing

This course will showcase a variety of topics from which students can select one, or come up with one of their own, and proceed to study it in depth. The students will make presentations of their findings to class by citing literature and code implementations where appropriate, and culminate with the writing of a scholarly paper on the topic chosen.

Elective Courses

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.

MSQC614: Quantum Information Theory

Quantum information theory synthesizes three major themes: quantum physics, computer science, and information theory. At the core of information theory lies the work of Claude E. Shannon, which we review in this course, and we present and study three problems related to his work and subsequent extension to quantum computing.

These are, compressing quantum information, transmitting classical and quantum information through noisy quantum channels, and quantifying, characterizing, transforming, and using quantum entanglement.

MSQC615: Quantum Thermodynamics

This course introduces quantum thermodynamic concepts and techniques relevant to quantum computing and quantum computing devices. Topics include a review of axiomatic thermodynamics, connections between information and thermodynamics, quantum information engines, dynamics in open systems, decoherence, dissipation, and quantum resource theory.

Additional Course

MSQC699: Independent Research in Quantum Computing