Graduate Quantum Computing Courses

COS531 Modern Applied Statistical Learning (3 credits)

Modern statistical learning focuses on the application of complex computing techniques and statistical inference to extract information from data to help develop trends and patterns, forecast potential future problems, and make business decisions. This course is designed to provide students with hands-on, practical experience in statistical learning methods such that they can apply them to solve real-world problems. Students enhance their understanding of statistical analysis and inference while getting trained on industry-standard software packages such as R. Prerequisite: DAS502

COS536 Applied Machine Learning (3 credits)

This is required course for Data Science Master’s Program. It extends some topics of DAS541 Data Mining for Business and focuses on the theoretical basis and applications of the state-of-the-art machine learning algorithms. Prerequisite: DAS541

COS541 Big Data Engineering (3 credits)

This course introduces students to the fundamental concepts, techniques, and tools used in managing and analyzing large volumes of data, focusing on data engineering principles and practices. Topics covered include data ingestion, storage, processing, and analysis in the context of big data technologies and platforms. Prerequisite: None

COS643 Computer Vision and Natural Language Processing (3 credits)

This course covers advanced topics on the latest developments in machine learning, focusing on the application of deep neural networks (deep learning) to computer vision and natural language processing. Prerequisite: COS536

QCI400 Overview of Quantum Computing (2 credits)

The objective of the course is to introduce to the students basic quantitative, mathematical and statistical methods for solving financial, marketing and business problems. Using Excel and Tableau, this course provides an introduction to data analytics for business professionals, including those with no prior analytics experience. Students will learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and develop the basic data literacy and analytics mindset needed to make appropriate business strategy recommendations based on insights from real- world data. Prerequisite: None

QCI401 Mathematical Foundations of Quantum Computing (3 credits)

This course provides a rigorous introduction to the mathematical foundations of quantum mechanics and quantum computing, which form the basis for understanding and designing quantum algorithms and quantum information protocols. Students proficient in linear algebra can test out of this course. Prerequisite: None

QCI501 Qubits, Quantum Gates and Quantum Circuits (3 credits)

This course provides an introduction to the principles of quantum computing and its underlying qubits, quantum gates, and quantum circuits. Students will learn the fundamental concepts of quantum mechanics necessary for understanding quantum computing, including the principles of superposition, entanglement, and measurement. Prerequisite: Co-Requisite: QCI401

QCI521 Foundational Quantum Algorithms (3 credits)

This course provides an introduction to the principles of quantum computing and the algorithms that take advantage of the unique properties of quantum mechanics to solve computational problems faster than classical algorithms. The course will cover the basic quantum algorithms, including Deutsch-Jozsa, Simon’s algorithm, and Grover’s algorithm, as well as advanced algorithms such as Shor’s and HHL. Throughout the course, students will use software tools such as Qiskit, Cirq, and Braket to design, simulate, and analyze quantum algorithms. Prerequisite: QCI501

QCI531 Practical Quantum Computing Applications (3 credits)

This course provides an introduction to the practical applications of quantum computing in various fields, such as quantum simulation, optimization, and machine learning. Throughout the course, students will use software tools such as Qiskit, Cirq, and Braket to design, simulate, and analyze quantum applications. Students will develop strong problem-solving skills and the ability to apply quantum computing concepts to real-world problems in various fields. Prerequisite: Co-Requisite: QCI521

QCI601 Quantum Computing Hardware and Systems (3 credits)

This course provides an introduction to the principles of quantum hardware and the systems used for quantum computing. Students will learn about the different types of quantum hardware, including superconducting qubits, trapped ions, and photonic qubits. The course will also cover the principles of quantum error correction and how it can be used to protect quantum information from noise and decoherence. Prerequisite: QCI501

QCI602 Advanced Quantum Mechanics (3 credits)

This course provides an in-depth exploration of advanced topics in quantum mechanics with a focus on their applications in quantum computing and quantum simulation. Topics covered include, for example, open systems and density matrices, second quantization, and Hartree-Fock approximation. Students will develop strong problem-solving skills and the ability to apply advanced quantum mechanics concepts to real-world problems in quantum computing and simulation. Prerequisite: QCI401

QCI621 Advanced Quantum Algorithms – Machine Learning (3 credits)

This course provides an in-depth exploration of the principles of quantum machine learning and its potential superiority over classical machine learning. Students will gain a comprehensive understanding of how quantum machine learning algorithms, including quantum support vector machines and quantum neural networks, can be effectively employed to solve intricate classification and regression problems. Prerequisite: QCI521

QCI641 Topics in Quantum Computing (3 credits)

This seminar course provides an in-depth exploration of the latest research and developments in quantum computing, including advancements in quantum hardware, quantum algorithms and their potential impact on various fields, and quantum software tools. Prerequisite: QCI531

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