Building a Professional Master's Program in Quantum Computing: Bridging Academic, Training and Industry Needs
Bibliographic record
Abstract
Since quantum technologies are quickly advancing to be used in actual applications, there is an increased demand to have graduate programs prepare recent graduates and practicing professionals from science and engineering disciplines to have competencies to enter the nascent quantum workforce. In this presentation, we detail key lessons learned from developing and launching the University of Calgary professional master's program in Quantum Computing, deliberately created to prepare students entering this rapidly evolving domain of employment. The presentation highlights the program's innovative curriculum, featuring hands-on lab experiences with tools like Qiskit, and a stream-based structure encompassing theory, software, hardware, and business. A distinctive strength of the program lies in its partnership with Quantum City-a Calgary-based initiative that connects academia, industry, and government to foster Alberta's quantum ecosystem. This collaboration provides students with valuable exposure to real-world applications and meaningful industry engagement. Educators, program developers, and academic leaders attending the presentation will leave with actionable recommendations and insights to build accessible, industry-focused quantum education programs to support today's quantum economy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".