Graduate Quantum Computing Courses
QCI400 Overview of Quantum Computing (3 credits)
This course offers a high-level overview of quantum computing, covering its history, advantages, challenges, future directions, fundamental principles, and applications. Students will gain hands-on experience solving basic problems using quantum simulators and real quantum computers accessible via the cloud.
QCI401 Foundational Linear Algebra and Probability (3 credits)
This course covers essential mathematical concepts in linear algebra and probability theory, providing the foundational tools necessary for understanding quantum computing.
QCI402 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.
QCI501 Qubits, Quantum Gates and Quantum Circuits (3 credits)
This course introduces the foundational principles of quantum computing, focusing on qubits, quantum gates, and quantum circuits. Students will explore key quantum mechanics concepts essential for understanding quantum computation, including superposition, entanglement, and measurement, providing a solid foundation for further study in the field.
QCI502 Quantum Entanglement and Quantum Information (3 credits)
Expanding on fundamental quantum principles, this course explores quantum entanglement and its applications—key phenomena that distinguish quantum computing from classical computing. Students will examine how entanglement enables quantum information processing and plays a crucial role in quantum algorithms and communication protocols.
QCI511 Quantum Computing Hardware and Systems (3 credits)
This course offers an introduction to the principles of quantum computing hardware and systems. Students will explore various types of quantum hardware, including superconducting qubits, trapped ions, and photonic qubits, gaining insight into their unique architectures and functionalities. The course also delves into the fundamentals of quantum error correction, examining how it safeguards quantum information from noise and decoherence, ensuring reliable quantum computations.
QCI521 Foundational Quantum Algorithms (3 credits)
This course introduces the principles of quantum computing and algorithms that leverage the unique properties of quantum mechanics to outperform classical methods in solving complex computational problems. Students will study foundational quantum algorithms, including Deutsch-Jozsa, Simon’s, Grover’s algorithms, as well as advanced algorithms like Shor’s and HHL. Through hands-on practice with tools such as Qiskit, Cirq, or Braket, students will design, simulate, and analyze quantum algorithms, gaining both theoretical knowledge and practical experience.
QCI522 Advanced Quantum Algorithms (3 credits)
This course offers an in-depth exploration of quantum machine learning, highlighting its principles and potential advantages over classical approaches. Students will develop a thorough understanding of quantum machine learning algorithms, such as quantum support vector machines and quantum neural networks, and learn how to apply these techniques to tackle complex classification and regression problems effectively.
QCI523 Practical Quantum Computing Programming (3 credits)
This course explores the practical applications of quantum computing across diverse fields, including quantum simulation, optimization, and machine learning. Students will gain hands-on experience with software tools like Qiskit, Cirq, or Braket to design, simulate, and analyze quantum-based solutions. By the end of the course, students will have developed strong problem-solving skills and the ability to apply quantum computing concepts to address real-world challenges in various domains.
QCI641 Topics in Quantum Computing Applications (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.
QCI651 Capstone Project (3 credits)
The Capstone Project course enables students to apply their knowledge of quantum computing by designing and executing a comprehensive project that addresses a real-world challenge. Students will engage in independent research and development, leveraging advancements in quantum hardware, algorithms, and software tools. The course emphasizes critical thinking, innovation, and the practical application of quantum computing concepts to deliver impactful solutions.