THE UNIVERSITY OF CALGARY Programming with a Quantum Stack
Bibliographic record
Abstract
This thesis presents the semantics of quantum stacks and a functional quantum pro-gramming language, L-QPL. An operational semantics for L-QPL based on quantum stacks in the form of a term logic is developed and used as an interpretation of quan-tum circuits. The operational semantics is then extended to handle recursion and alge-braic datatypes. Recursion and datatypes are not concepts found in quantum circuits, but both are generally required for modern programming languages. The language L-QPL is introduced in a discussion and example format. Various example programs using both classical and quantum algorithms are used to illustrate features of the language. Details of the language, including handling of qubits, general data types and classical data are covered. The quantum stack machine is then presented. Supporting data for operation of the machine are introduced and the transitions induced by the machine’s instructions are given. ii Acknowledgments Anyone who has written a thesis knows that an incredible number of people support and inspire the writer. I have been incredibly fortunate to have many gifted people in my life during this period. My greatest appreciation to Dr. Robin Cockett for working with me these past years. His tireless efforts and indefatigable enthusiasm are a constant source of inspi-ration. Thanks to Robin Cockett, Peter Høyer and Barry Sanders for serving on my thesis committee. Thank you to Peter Selinger for support and encouragement during the initial de-velopment and experimentation with quantum programming languages. Thank you to my many proof-readers from the programming languages lab, Xi-
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.019 |
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".