Denoising Wavelength-Multiplexed Time-Bin Correlated Photons for Quantum Networks
Bibliographic record
Abstract
We present the capabilities of the quantum Talbot array illuminator (qTAI) for denoising wavelength-multiplexed time-bin correlated photons that would be employed in a quantum network. Incoherent noise, exhibiting fast amplitude and phase variations, represents a major obstacle towards the development of quantum communication technologies. This is particularly challenging when the photons have a short spectral bandwidth, such as generated by atomic ensembles or microring resonators, because it remains challenging to conceive low-loss, high-rejection filters with sub-GHz bandwidths. Using electro-optic temporal phase modulation and dispersive propagation, the qTAI implements a coherent energy redistribution scheme the coherent biphoton wavefunction is redistributed into narrow peaks. In contrast, the incoherent noise background remains virtually unaffected by these manipulations, such that the relevant biphoton signal can be discerned by the noisy background, enabling noise mitigation with ~1.5 GHz bandwidth. Here we show the improvement of the coincidence-to-accidental ratio (CAR), which is directly related to the quantum bit error rate (QBER), for various pump powers and noise injection rates. Two wavelength-multiplexed quantum channels are simultaneously denoised using a single qTAI device, which denoises the quantum signals without the need for spectral or temporal alignment beyond usual time-bin measurement approaches. Our results demonstrate that the qTAI can enable secure communications in channels that would otherwise be too noisy to allow for a quantum-secured link.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".