An Overview of Quantum Information Science Courses at US Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the field of Quantum Information Science (QIS) continues to advance, there is an increased need for a quantum-smart workforce to address the needs of the growing quantum industry. As institutions begin to expand their course offerings, there is a unique opportunity for discipline-based education researchers to have an impact on the curricular and pedagogical choices being made in these courses. As a first step, it is necessary for education researchers to have a representative picture of what QIS education currently looks like. We reviewed recent course catalogues from a large sample of institutions in the United States looking for courses focused on QIS content. Our conservative analysis reveals that roughly a quarter of the institutions we reviewed offer QIS courses. While encouraging for such an emerging field, we found disparities in the types of institutions offering these courses as the vast majority were Doctoral-granting institutions. Additionally, we found that some classifications of minority serving institutions were much less likely to offer a QIS course (for example Historically Black Colleges and Universities or Predominantly Black Institutions), while Asian American and Native American Pacific Islander serving institutions were more likely than the national average to offer a QIS course. These disparities may lead to further racial, socioeconomic, and geographic disparity in the future quantum workforce. We also found that there was no single department that offered a majority of the QIS courses, indicating that the best efforts to improve QIS education will need to consider the multi-disciplinary nature of the field of quantum information science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it