Noiselessly amplified thermal states and after multi-photon addition or subtraction
Bibliographic record
Abstract
In this paper, we study the combined effects of the ideal amplification operator ([Formula: see text]) and m-photon added operator [Formula: see text] (or m-photon subtracted operator [Formula: see text]) on the thermal state ([Formula: see text]), with mean photon number (MPN [Formula: see text]). By only operating [Formula: see text] on [Formula: see text], we introduce a noiselessly amplified thermal state (ATS [Formula: see text]) with larger MPN [Formula: see text]. The selection of [Formula: see text] and g must ensure that the process is physical ([Formula: see text]). By operating [Formula: see text] and [Formula: see text] (or [Formula: see text]) on [Formula: see text] successively, we further introduce photon-added-ATS (ρ m+) or photon-subtracted-ATS (ρ m−). Then, we analyze their photon number distributions and prove that the purity of ρ m+ is equal to that of ρ m−. Meanwhile, we study their nonclassicality by examing Mandel parameter and second-order correlation function. Finally, we study their three quasiprobability distributions, whose negativities are other signatures of the nonclassicality. Our study demonstrates that photon addition has more pronounced effects on nonclassicality than photon subtraction. Our study will provide useful theoretical references for experiments and applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".