Wave packets in a sieve: quantum control at the edge of strong chaos
Bibliographic record
Abstract
We consider the quantum dynamics of the kicked rotor model in the regime where the last stable resonance islands in the classical phase space disappear. It is found that phase space regions with lower local diffusion rates are able to preserve quantum population. The low-diffusion regions can be moved through phase space in a quasi-adiabatic way by smoothly varying the parameters of the kicking field. With appropriately chosen time-scales for the variation of the field parameters, wave packets initially localized around the low-diffusion areas remain localized around these regions as the regions are moved. The wave packets can then be carried to desired regions of space despite the predominantly chaotic character of the classical phase space. We also comment on the Gong–Brumer scenario of coherent control of quantum chaotic diffusion [Phys. Rev. Lett. 86 1741 (2001)]. While our phase-space inspired control has a clear semiclassical interpretation, it appears that the Gong–Brumer control has a predominantly quantum origin. Furthermore, it is found that our control over wave packet localization disappears in the deeply chaotic regime, while the Gong–Brumer control over the chaotic diffusion persists in this regime but becomes increasingly unstable with increasing chaoticity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".