Quantum control : mathematical and numerical challenges : CRM workshop, October 6-11, 2002, Montréal, Canada
Bibliographic record
Abstract
Molecular alignment and orientation: From laser-induced mechanisms to optimal control by O. Atabek and C. M. Dion Overview and software guide of evolutionary algorithms A case study in quantum control by A. Auger, A. Ben Haj-Yedder, and M. Schoenauer Laser control of molecular states--Nonperturbative examples by A. D. Bandrauk, F. Legare, and H. T. Yu Coherent control: Principles and semiclassical implementations by V. S. Batista and P. Brumer Mathematical models of contemporary elementary quantum computing devices by G. Chen, D. A. Church, B.-G. Englert, and M. S. Zubairy Addendum and remarks on doubly conservative numerical schemes for the nonlinear Schrodinger equation and its control by M. C. Delfour A note on the exact internal control of nonlinear Schrodinger equations by R. Illner, H. Lange, and H. Teismann Towards efficient numerical approaches for quantum control by C. Le Bris, Y. Maday, and G. Turinici Multichannel quantum defect study of the control in the frequency domain: Example of HI by H. Lefebvre-Brion Development of solution algorithms for quantum optimal control equations in product spaces by Y. Ohtsuki and H. Rabitz Using contracted basis functions to solve the Schrodinger equation by X.-G. Wang and T. Carrington, Jr. Remarks on the controllability of the Schrodinger equation by E. Zuazua.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.030 |
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".