A Sparse-Event Simulation Engine to Model Coincidence-Based Ranging Architectures in Quantum Lidar
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Bibliographic record
Abstract
Non-classical radar and lidar systems have received substantial interest recently; however, while the many experimental demonstrations have provided deep physical knowledge of such systems, there remains a lack of effective system models to obtain fundamental metrics such as range resolution as a function of system parameters. This work introduces a high-fidelity simulation platform to mimic a certain type of quantum radar, specifically a recently proposed one based on temporal coincidences that arise due to entanglement. Specifically, the system measures coincidences between events related to a reference source and those related to the back-scattering of photons from targets. The large number of events – and their complex interaction with system components – makes a realistic simulation challenging. As an initial assessment, in this paper we develop a simulator to estimate the expected point spread function (PSF), and thereby the range resolution, considering various coincidence window time widths and system non-idealities. The estimate is based on the numerical computation of the correlation between the reference traces shifted along the time domain and traces of back-scattered photons (along with noise photons). The simulated results are comparable to available experimental results, illustrating the fidelity of the simulation engine. A crucial result is that, unlike a classical radar, the PSF and range resolution depends upon the environmental noise and multiple system parameters, not just the transmitted waveform.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it