- Overview
- Learning Outcomes
- Requirements
- Curriculum Details
Program Purpose
The Master of Science in Quantum Computing program seeks to provide students with a comprehensive education in the principles and applications of quantum computing. Through a combination of theoretical and hands-on coursework, students will develop a strong foundation in quantum mechanics, computer science, and mathematics, and learn how to apply these principles to the design and analysis of quantum computing systems.
Educational Objectives
The Master of Science in Quantum Computing program aims to:
- Students will develop a strong foundation in the mathematical principles of quantum mechanics, including linear algebra and probability theory.
- Students will develop a strong foundation in the principles of quantum algorithms and their implementation on quantum hardware, to gain fluency in statistical programming languages and big data tools through coursework, projects and applied research.
- Students will be able to implement practical applications of quantum computing and analyze their advantages and limitations, as well as to program and simulate quantum computers using languages such as Qiskit, Cirq, and Bracket.
Faculty
Program Learning Outcomes
After Completing this program, students should:
- Students will develop a strong foundation in the mathematical principles of quantum mechanics, including linear algebra and probability theory.
- Students will develop a strong foundation in the principles of quantum algorithms and their implementation on quantum hardware, to gain fluency in statistical programming languages and big data tools through coursework, projects and applied research.
- Students will be able to implement practical applications of quantum computing and analyze their advantages and limitations, as well as to program and simulate quantum computers using languages such as Qiskit, Cirq, and Bracket.
Admissions Requirement
Institutional-wide Admission Criteria
- Completion of undergraduate degree
- Official Transcript: allow for evaluation of academic performance, relevant coursework, and overall readiness for college-level study.
- Personal Statement: helps reviewers understand the applicant’s motivations and aspirations to pursue the program of study.
- CV: presents the academic and professional history of the applicant
- Letters of Recommendation: Letters of recommendation from teachers, mentors, or professionals familiar with the applicant’s abilities and potential, and additional insights into the applicant’s character, work ethic, and potential for success in the program.
Program-specific Criteria
- All applicants to the MS in Quantum Computing should have an undergraduate degree in related fields such as physics, mathematics, computer science, or engineering.
- Applicants are required to have a minimum undergraduate GPA of 2.7 to be considered for admission.
Graduation Requirement
- Successful completion of the curriculum with a grade point average of no less than B minus (2.7).
- Take at least 50% of required credits from FTC Northern.
- Complete the MS degree within 4 years of matriculation to the program.
Curriculum Overview
The MS in Quantum Computing is a 33-semester credit curriculum with three major components: core requirements, electives, and a capstone project.
MS in Quantum Computing Curriculum
| Area | Credits |
|---|---|
| Core Requirements | 23 |
| Foundation | 9 |
| Quantum Algorithms | 6 |
| Applications and Special Topics | 6 |
| Electives | 6 |
| Capstone | 4 |
| Total Required Credits for Graduation | 33 |
Curriculum Details
| Code | Course Title | Credits | Prerequisite(s) |
|---|---|---|---|
| Core Requirements (23 cr) | |||
| QCI400 | Overview of Quantum Computing | 2 | None |
| QCI401 | Mathematical Foundations of Quantum Computing | 3 | None |
| QCI501 | Qubits, Quantum Gates and Quantum Circuits | 3 | Co-Requisite: QCI401 |
| QCI521 | Foundational Quantum Algorithms | 3 | QCI501 |
| QCI531 | Practical Quantum Computing Applications | 3 | Co-Requisite: QCI521 |
| QCI601 | Quantum Computing Hardware and Systems | 3 | QCI501 |
| QCI621 | Advanced Quantum Algorithms – Machine Learning | 3 | QCI521 |
| QCI641 | Topics in Quantum Computing | 3 | QCI531 |
| Electives (Select two from the following)(6 cr) | |||
| COS531 | Modern Applied Statistical Learning | 3 | DAS502 |
| COS541 | Big Data Engineering | 3 | None |
| QCI602 | Advanced Quantum Mechanics | 3 | QCI401 |
| COS536 | Applied Machine Learning | 3 | DAS541 |
| COS643 | Computer Vision and Natural Language Processing | 3 | COS536 |
| Capstone Project (4 cr) | |||
| QCI651 | Capstone Project | 4 | QCI641 |
| Total Credits Required for Graduation | 33 | ||
