High-resolution electro-optic sampling of biphoton states
Bibliographic record
Abstract
High temporal resolution detection for time-correlated single-photon counting (TCSPC) is critical for a broad range of applications, such as sensing, bio-imaging and quantum information. To harness non-classical advantages, high temporal resolution TCSPC is necessary to capture the unique properties of quantum entanglement, in which the precise time delays between two photons are used to reconstruct the biphoton distribution. However, current state-of-the art, high-resolution TCSPC systems, such as superconducting nanowires, have large footprints and require cryogenic cooling to liquid helium temperatures. They are not well equipped to be conveniently mounted on a satellite or transported within a health care facility. Small footprint, simple, low energy consuming single photon detection systems are therefore needed in order for high temporal resolution TCSPC applications to move beyond the research laboratory. In this direction, we demonstrate a proof-of-concept experiment for improving the temporal resolution of single-photon and biphoton detection schemes that is simple, fiber-based, and readily chip integrable. The principle relies on electrooptic gating of fast single-photon and biphoton signals using a high-speed RF pulse which drives an electro optic intensity modulator. As such, the instrument response function (IRF) of the detection scheme takes on the temporal profile of the electro-optic gate. Experimentally, we improve the IRF of our detection scheme from ~1.54 ns to <100 ps, allowing high resolution detection of ultrafast single photon TCSPC signals as well as to observe nonlocal dispersion cancellation effects in ultrafast biphoton distributions. This technique could allow for practical and simplified access to rapid temporal dynamics at the single photon scale.
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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 | 0.000 |
| 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".