Generation of non-Gaussian states of light using deterministic photon subtraction
Bibliographic record
Abstract
Abstract We explore a recently demonstrated deterministic photon subtraction scheme, based on single-photon Raman interaction with a Λ-type three-level atom, as a tool for manipulating quantum state of few-photon light pulses. We establish a comprehensive theoretical framework using input–output formalism and quantum regression theorem, enabling calculation of the first order autocorrelation matrices of the output light and identification of the temporal modes present in the generated light via their eigendecomposition. By modeling the entire system as a quantum network consisting multiple virtual cavities and a lambda-type emitter cascaded in two parallel guided modes of opposite propagation directions, we extract the quantum state occupying the modes of interest. For both squeezed vacuum and coherent light input pulses, the Wigner function of the output light after photon subtraction clearly reveals its non-Gaussian character. Furthermore, we propose a measurement-based scheme on the subtracted photon which can lead to conditional generation of quantum states resembling Schrodinger’s kitten state directly from coherent input light with fidelities above 99%. This result is particularly nothworthy, as coherent pulses, unlike the squeezed vacuum inputs commonly used in previous studies, are readily available in most experimental setups.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".