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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:


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.


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.
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