Dev
Entangled Photon Microscopy — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| pair_generation_rate | -2.4 – 3.6 | - |
| coincidence_window | -1.16 – 4.24 | ns |
| accidental_coincidence_rate | -4.8 – 7.2 | - |
| photon_loss_(per_arm) | -1.44 – 2.16 | dB |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinGhost + gradient | 0.763 | 32.13 | 0.944 | 0.82 | ✓ Certified | Wang et al., npj Quantum Inf. 2023 |
| 2 | DiffGhost + gradient | 0.732 | 29.68 | 0.912 | 0.85 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 3 | TransGhost + gradient | 0.701 | 27.8 | 0.877 | 0.86 | ✓ Certified | Li et al., Opt. Express 2022 |
| 4 | PhysGhost + gradient | 0.694 | 27.14 | 0.862 | 0.89 | ✓ Certified | Chen et al., Phys. Rev. Lett. 2024 |
| 5 | GAN-Ghost + gradient | 0.622 | 24.44 | 0.784 | 0.82 | ✓ Certified | Wang et al., Phys. Rev. A 2019 |
| 6 | DnCNN-Ghost + gradient | 0.542 | 21.56 | 0.671 | 0.79 | ✓ Certified | Lyu et al., Optica 2017 |
| 7 | SVD-Ghost + gradient | 0.448 | 17.99 | 0.5 | 0.84 | ✓ Certified | Gong et al., Sci. Rep. 2010 |
| 8 | Coincidence-Count + gradient | 0.416 | 16.66 | 0.433 | 0.88 | ✓ Certified | Pittman et al., Phys. Rev. A 1995 |
| 9 | CS-Ghost + gradient | 0.404 | 16.53 | 0.427 | 0.84 | ✓ Certified | Katz et al., Appl. Phys. Lett. 2009 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%