MPS in Quantum Computing
The Science Academy, housed in the College of Computer, Mathematical, and Natural Sciences (CMNS), draws on the university’s collective expertise to provide academic programs that are both rigorous and relevant. Science Academy Graduate Programs translate research into applied knowledge and provides current and future professionals with invaluable skills.
Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. Students should contact the contact Science Academy staff via email: scienceacademy@umd.edu.
Overview
The Master of Professional Studies in Quantum Computing, In Person (MPQC) is a 30-credit, 10-course, non-thesis graduate program provides students with foundational, practical and theoretical topics of quantum computing.
- Students discover current state-of-the-art quantum computing technology and areas of application, and explore origins, evolution, and possible future states of this technology.
- Experiential learning is core and courses provide students with ample opportunities to apply concepts on current-day commercial quantum computing hardware.
- Prepares students to apply the principles and techniques of quantum computing to the solution of a variety of problems in optimization, secure communications, encryption, materials discovery and any such problems that require considerable computing resources.
- Students learn to differentiate the many technologies currently used to implement quantum computers and compare their intrinsic strengths and limitations.
- Can be completed in sixteen months of continuous full-time enrollment. See Designation of Full-time/Part-time Status.
Program Features
- Curriculum required 30 credits (10 courses), including 7 core courses and 3 elective courses.
- Special topics include quantum networks, quantum thermodynamics, quantum machine learning, and quantum computing hardware.
Courses
Below is a listing of all program courses. For a detailed course description that includes pre-requisites or co-requisites, see The Graduate School Catalog, Course Listing as follows: MSQC Course Descriptions (Coming soon).
| Type | Course Number | Title |
|---|---|---|
| Core | MSQC601 | The Mathematics and Methods of Quantum Computing |
| Core | MSQC602 | Physics of Quantum Devices |
| Core | MSQC603 | Principles of Machine Learning |
| Core | MSQC604 | Quantum Computing Architectures and Algorithms |
| Core | MSQC605 | Advanced Quantum Computing and Applications |
| Core | MSQC606 | Practical Quantum Computing |
| Core | MSQC607 | Advanced Topics in Quantum Computing |
| Elective | MSQC610 | Quantum Machine Learning |
| Elective | MSQC611 | Quantum Networks |
| Elective | MSQC612 | Quantum Computing Hardware |
| Elective | MSQC613 | Quantum Monte Carlo and Applications |
| Elective | MSQC614 | Quantum Information Theory |
| Elective | MSQC615 | Quantum Thermodynamics |
Registration Overview
- See the sample plan of study, below. Students should use this as a guide to develop a plan with the academic program director.
- Actual course offerings are determined by the program and may vary semester to semester. Students should note if a course has a pre-requisite or co-requisite.
- Specific class meeting information (days and time) is posted on UMD’s interactive web service services, Testudo. Once on that site, select “Schedule of Classes,” then the term/year. Courses are listed by academic unit.
- The program uses specific section codes for registration, which are listed on the sample plan of study.
Sample Plan, Full-Time
| Semester | Year | Course Number | Section Code | Credits |
|---|---|---|---|---|
| Fall | 1 | MSQC601 | PCS* | 3 |
| Fall | 1 | MSQC602 | PCS* | 3 |
| Fall | 1 | MSQC603 | PCS* | 3 |
| Spring | 1 | MSQC604 | PCS* | 3 |
| Spring | 1 | MSQC606 | PCS* | 3 |
| Spring | 1 | MSQC610 | PCS* | 3 |
| Summer | 1 | MSQC612 | PCS* | 3 |
| Fall | 2 | MSQC605 | PCS* | 3 |
| Fall | 2 | MSQC607 | PCS* | 3 |
| Fall | 2 | MSQC615 | PCS* | 3 |
Sample Plan, Part-Time
| Semester | Year | Course Number | Section Code | Credits |
|---|---|---|---|---|
| Fall | 1 | MSQC601 | PCS* | 3 |
| Fall | 1 | MSQC602 | PCS* | 3 |
| Spring | 1 | MSQC603 | PCS* | 3 |
| Spring | 1 | MSQC604 | PCS* | 3 |
| Summer | 1 | MSQC612 | PCS* | 3 |
| Summer | 1 | MSQC613 | PCS* | 3 |
| Fall | 2 | MSQC605 | PCS* | 3 |
| Fall | 2 | MSQC607 | PCS* | 3 |
| Spring | 2 | MSQC606 | PCS* | 3 |
| Spring | 3 | MSQC610 | PCS* | 3 |
In-Person Learning
- Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment.
- Classes held weekday evenings (e.g., after 5:00 p.m.) to accommodate the working professional’s schedule.
- Uses the semester academic calendar with classes held in fall and spring semester (16 weeks each) and Summer Session (two 6-week sessions).
- Instruction provided by University of Maryland faculty and professionals in the field.
- Instructors present dynamic and interactive seminar-style instruction.
- Students enrolled in a program that features in-person instruction are required to submit the University’s Immunization Record Form prior to the first day of their first semester/term. See Health Requirements.
Upon successful completion, graduates will have mastered the following competencies:
- Explain principles of quantum physics as they apply to quantum computing.
- Develop quantum computing programs and implement them on quantum computing platforms.
- Distinguish the elements of a quantum computing algorithm and differentiate it from a classical algorithm.
- Describe current quantum computing hardware, and examine the effects of its current state of maturity on the design of quantum computing algorithms.
- Discuss and implement quantum computing paradigms to solve problems in quantum networks and quantum machine learning.
- Compare quantum thermodynamics and quantum information theory and how they relate to classical information theory.