Observing quantum coherence from photons scattered in free-space
Bibliographic record
Abstract
Abstract Quantum channels in free-space, an essential prerequisite for fundamental tests of quantum mechanics and quantum technologies in open space, have so far been based on direct line-of-sight because the predominant approaches for photon-encoding, including polarization and spatial modes, are not compatible with randomly scattered photons. Here we demonstrate a novel approach to transfer and recover quantum coherence from scattered, non-line-of-sight photons analyzed in a multimode and imaging interferometer for time-bins, combined with photon detection based on a 8 × 8 single-photon-detector-array. The observed time-bin visibility for scattered photons remained at a high 95% over a wide scattering angle range of −450 to +450, while the individual pixels in the detector array resolve or track an image in its field of view of ca. 0.5°. Using our method, we demonstrate the viability of two novel applications. Firstly, using scattered photons as an indirect channel for quantum communication thereby enabling non-line-of-sight quantum communication with background suppression, and secondly, using the combined arrival time and quantum coherence to enhance the contrast of low-light imaging and laser ranging under high background light. We believe our method will instigate new lines for research and development on applying photon coherence from scattered signals to quantum sensing, imaging, and communication in free-space environments.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".