Education and workforce
Through coursework and research integration, students gain experience working with real spectroscopic data, computational modeling, and machine learning methods. This training prepares students to contribute to emerging fields including quantum sensing, quantum simulation, and AI-enabled scientific discovery.
From graduate courses in quantum computing foundations to undergraduate lab modules that translate biological and geoscientific problems onto quantum hardware, the Schmidt College of Science is weaving quantum training across the curriculum.
Quantum Across the Disciplines
Hands-on quantum experience, woven into courses across seven departments.
Computational Physics with Quantum Computing Module
- PHZ 5156
Professor Warner Miller teaches this graduate-level course, which includes dedicated quantum computing content. Students gain direct access to D-Wave's Leap platform through the College's partnership, providing hands-on experience with quantum annealing for physics problems.
Climate and Geoscience Quantum Modules
- MET 4142
D-Wave's Leap quantum cloud service is being integrated into the Climate Data Applications course (MET 4142) and a prospective new GeoAI course, exposing students to quantum and hybrid computing concepts through applied geoscience examples including atmospheric hazard modeling and wildfire landscape simulation.
Post-Quantum Cryptography Research Training
Graduate students and postdoctoral researchers in the Department of Mathematics and Statistics receive training in post-quantum cryptographic constructions— lattice-based, code-based, and isogeny-based schemes — with access to HPC resources for classical benchmarking and prospective access to quantum hardware for experimental validation of hardness assumptions.
Quantum Computing Integration in Biology Courses
Ìý- BSC 4884 · IDS 4139 · BSC 6895
Two biology courses are being developed to incorporate quantum and hybrid computing modules:
BSC 4884 — Introduction to Biological Networks: a lab module translating biological network problems into QUBO formulations and benchmarking classical vs. hybrid quantum approaches.
IDS 4139 / BSC 4930 / BSC 6895 — AI Applications in Biology: a hands-on unit on hybrid quantum optimization and sampling for model selection and inference.
Physical Chemistry and Quantum Foundations in Chemistry
Undergraduate and graduate instruction in Physical Chemistry provides foundational training in thermodynamics, kinetics, quantum mechanics, and spectroscopy—core principles underpinning quantum science and technology.
- Physical Chemistry I (Thermodynamics and Kinetics): Emphasizes energy, entropy, transport, and dynamical processes relevant to complex and quantum systems.
- Physical Chemistry II (Quantum Chemistry and Spectroscopy): Introduces quantum mechanics through molecular systems, including wavefunctions, operators, and spectroscopic observables as direct manifestations of quantum behavior.
These courses form a natural bridge between traditional chemical education and modern quantum science applications.
Urban Planning Quantum Computing Training
Department of Urban and Regional Planning
Faculty and students in the Department of Urban & Regional Planning are undertaking D-Wave quantum computing training courses, with focused exploration of how Leap cloud services can enhance transportation optimization, urban morphology simulation, and disaster response research. Integration into existing and new courses is actively planned.
Future Curriculum
Emerging Courses in Quantum and Data-Driven Chemistry
Planned course development will expand training at the intersection of chemistry, data science, and quantum technology, including:
- Quantum Science through Molecular Systems
- Machine Learning for Physical and Quantum Chemistry
- Spectroscopy, Data, and Inverse Problems
- Statistical Thermodynamics for Complex and Quantum Systems
These offerings will support interdisciplinary workforce development by preparing students to work across chemistry, physics, and data-driven quantum science.
New Quantum Computing Courses in Mathematics
MAD 4513 · MAD 4515 · MAD 5616 · MAD 6517
Four mathematics graduate and undergraduate courses have been developed to study the foundations of quantum computing:
MAD 4513 — Mathematical Introduction to Quantum Algorithms
MAD 4515 — Mathematics of Quantum Computing and Data Analysis
MAD 5616 — Mathematical Foundations of Quantum Algorithms
MAD 6517 — Mathematical Methods in Quantum Computing
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