Towards arbitrary time-frequency mode squeezing with self-conjugated mode squeezing in fiber
Bibliographic record
Abstract
Optical parametric amplification generates squeezed light in device-specific sets of time-frequency eigenmodes, and it has been widely accepted that detection and utilization of squeezing must comply with this modal constraint. We show that this constraint can be considerably relaxed under the continuous-wave pump and broadband phase-matching approximation, where the modal decomposition is non-unique. Specifically, any time-frequency mode with “self-conjugated” spectral symmetry can approximate a squeezing eigenmode, and partial homodyne detection can herald squeezing in arbitrary time-frequency modes. We demonstrate this using a high-efficiency, low-loss all-fiber source, measuring 4.38 ± 0.11 dB and 0.88 ± 0.09 dB squeezing on partially coherent and chaotic self-conjugated modes, respectively. Using a bichromatic self-conjugated mode with reduced local-oscillator noise, we achieve 7.50 ± 0.12 dB squeezing, which represents the highest level reported for fully guided-wave squeezing sources based on χ(2) and χ(3) nonlinearities. Squeezed light enhances precision in quantum technologies. Here, authors demonstrate an all-fiber source achieving 7.5 dB squeezing in self-conjugated modes, enabling reconfigurable quantum light in arbitrary time-frequency modes using standard telecom components.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".