Quantum Computing for High-School Students An Experience Report
Bibliographic record
Abstract
Quantum computing is an emerging field that can revolutionize our ability to solve problems and enable breakthroughs in many areas including optimization, machine learning, chemistry, and drug design. With the increasing computational power of quantum computers and the proliferation of quantum development kits, the demand for a skilled workforce in quantum computing increases significantly. The theory of quantum computing lies at the crossroads of quantum physics, mathematics, and computer science. The field of quantum computing has matured and can now be explored by all students. While today, quantum computers and simulators are readily accessible and programmable over the internet, quantum computing education is just ramping up. This paper describes our experiences in organizing and delivering quantum computing workshops for high-school students with little or no experience in the abovementioned fields. We introduce students to the world of quantum computing in innovative ways, such as newly designed “unplugged” activities for teaching basic quantum computing concepts. Overall, we take a programmatic approach and introduce students to the IBM Q Experience using Qiskit and Jupyter notebooks. Our experiences and findings suggest that basic quantum computing concepts are palatable for high-school students, and-due to significant differences between classical and quantum computing-early exposure to quantum computing is a valuable addition to the set of problem-solving and computing skills that high-schoolers obtain before entering university.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".