Photon Number Resolution via Multi-Photon Subtraction in a Waveguide-QED Setting
Bibliographic record
Abstract
A description of states of light in terms of photons allows for a clear delineation between classical and non-classical light. The development of single-photon sources and detectors has enabled experimental studies of quantum light at the single-photon level, proving essential for applications in quantum communication, sensing, and imaging. Multi-photon quantum states, existing in higher-dimensional spaces, extend these applications and open opportunities to study the rich space of non-classical light states. Platforms for sourcing and characterizing such states have been increasingly explored to leverage the quantum resources they offer. Arithmetic operations of photon addition and subtraction, allow for generating non-classical multi-photon light from classical sources like coherent and squeezed states, as well as for photon-number-resolved (PNR) detection. PNR detection is essential for characterizing multi-photon states,providing access to their photon-number distributions and higher-order correlations, and enabling advanced quantum tomography.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".