Boston University offers New England\u2019s first master’s in quantum science. This interdisciplinary program integrates physics, chemistry, computer science, mathematics, electrical and computer engineering, and materials science engineering.<\/p>\n
Students learn from and are mentored by faculty at the forefront of quantum computing, quantum materials, sensing, cryptography, computational methods, and device engineering.<\/p>\n
The program is structured to ensure that all graduates leave with the following skills:\u200b<\/p>\n
At BU, discovery extends beyond the classroom. Through a required research capstone or industry internship, students apply classroom knowledge to real-world challenges using quantum hardware and computing. A newly designed laboratory course further enhances hands-on learning in BU\u2019s state-of-the-art quantum lab, ensuring that graduates are fully prepared for the demands of the quantum industry.<\/p>\n
The program guides students through core and elective courses (32 units total) over one year on campus as a full-time student. Program requirements are:<\/p>\n
The core courses, designed by our expert faculty, build the foundation of quantum science. These courses include:<\/p>\n
By the end of the course, students understand the mathematical and computational framework underlying quantum computing and why it represents a fundamentally new model of computation.<\/p>\n
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This companion course focuses on how quantum computing is physically realized in the laboratory. Students learn how real-world systems function as qubits, how quantum gates are implemented using electromagnetic fields and pulse sequences, how quantum states are prepared, controlled, and measured, and how physical errors are mitigated\/corrected. The course also surveys leading experimental platforms for quantum computing and sensing, including nitrogen-vacancy (NV) centers in diamond, neutral atom arrays, trapped ions, and superconducting circuits.<\/p>\n
<\/span><\/p>\n By the end of the course, students gain a practical understanding of how quantum devices are engineered and the technological challenges involved in building scalable quantum systems.<\/p>\n <\/div>\n<\/div>\n This hands-on laboratory course completes the core sequence by giving students direct experience with building quantum \u00a0hardware. Students develop practical skills needed to operate, design, and measure real-world quantum systems, including radio-frequency circuit design, pulse sequence programming, and photon counting. Using these skills, they implement basic single and two-qubit gates, quantum sensing protocols, and pulse sequences to mitigate the effects of quantum errors.\u00a0The course culminates in a capstone project centered on the experimental realization of a quantum platform based on nitrogen-vacancy (NV) centers in diamond.<\/span><\/p>\n <\/p>\n <\/span><\/p>\n By the end of the course, students gain hands-on experience bridging theory and hardware, developing the experimental and technical skills needed to work with quantum technologies in research and industry.<\/p>\n <\/div>\n<\/div>\n<\/p>\n In addition to the core curriculum, students select four elective courses across the five disciplines of Computer Science, Mathematics, Chemistry, Physics, and Engineering, allowing them to tailor their studies to their professional goals and interests.<\/p>\n <\/div>\n<\/div>\n <\/div>\n<\/div>\n <\/div>\n<\/div>\n <\/div>\n<\/div>\n <\/div>\n<\/div>\n<\/p>\n Students have the option to either engage with a participating BU research group<\/a>, or do an industrial internship, drawing on our industrial partnerships<\/a>. This provides practical training in an area of direct relevance while also building industry connections.<\/p>\n If a student does not opt for a research project or industry internship, they may acquire the additional two credits through another elective course.<\/p>\nPY 582 Quantum Laboratory (4 credits)<\/h4>
\n<\/span><\/p>\nElectives<\/h6>\n
Computer Science<\/h4>
\n\n
\n CAS CS 538<\/td>\n Fundamentals of Cryptography<\/td>\n<\/tr>\n \n CAS CS 548<\/td>\n Advanced Cryptography<\/td>\n<\/tr>\n \n CAS CS 599<\/td>\n Quantum Information Theory<\/td>\n<\/tr>\n \n CAS CS 535<\/td>\n Complexity Theory<\/td>\n<\/tr>\n \n CAS CS 630<\/td>\n Algorithms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Math<\/h4>
\n\n
\n CAS MA 569<\/td>\n Optimization Methods of Operations Research<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Chemistry<\/h4>
\n\n
\n CAS CH 651<\/td>\n Molecular Quantum Mechanics I<\/td>\n<\/tr>\n \n CAS CH 562<\/td>\n Molecular Quantum Mechanics II<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Physics<\/h4>
\n\n
\n CAS PY 502<\/td>\n Computational Physics<\/td>\n<\/tr>\n \n CAS PY 511<\/td>\n Quantum Mechanics I<\/td>\n<\/tr>\n \n CAS PY 512<\/td>\n Quantum Mechanics II<\/td>\n<\/tr>\n \n CAS PY 541<\/td>\n Statistical Mechanics 1<\/td>\n<\/tr>\n \n CAS PY 580<\/td>\n Machine learning for physicists<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Engineering<\/h4>
\n\n
\n CAS EC 531<\/td>\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n