Quantum correlated image recording through noisy and turbulent channels
Bibliographic record
Abstract
Various quantum imaging techniques have been shown to be effective at imaging through some aspects of traditionally difficult free-space channels, including ghost imaging through turbulent channels or quantum illumination through channels with noisy backgrounds. While effective, these techniques have only ever been shown to work independently, whereas real-world free-space channels are often both turbulent and noisy. This work experimentally demonstrates that quantum correlated imaging using a spontaneous parametric downconversion source and a time-tagging camera can be made robust against both turbulent media and a noisy background in free-space channels by implementing filtering based on the temporal and spatial correlations of paired photons. Furthermore, the filtering reduces accidental coincidence counts between uncorrelated photons, allowing the pair source to operate at high brightness which, in turn, leads to video-rate integration times. These quantum correlated recordings allow for improved object tracking, while the longer integration time images improve image fidelity over turbulent and noisy channels. This demonstration could allow for new improvements in communication, measurement, and sensing through turbulent and noisy free-space channels.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".