Experimental validation of the diagonal optical path properties: mitigating phase errors in interferometric-based optical processors
Bibliographic record
Abstract
We present an efficient calibration and programming methodology in the presence of imperfections and uncertainties for Mach-Zehnder interferometer (MZI)-based optical processors, utilizing the diagonal optical path properties. This approach enables direct phase monitoring of MZI phase shifters, inherently suppressing calibration errors caused by spurious scattered light originating from non-diagonal blocks and eliminating the need for computationally intensive calibration/programming schemes. We experimentally validate these properties using a 4 × 4 interferometric mesh fabricated on a silicon-on-insulator platform, demonstrating that the calibration remains unaffected by phase-setting uncertainties in preceding or succeeding blocks on a diagonal path. We also present a benchmarking procedure to assess testbed fidelity, which is further used to confirm the effectiveness of our approach by programming two random weight matrices, where fine-tuning via the diagonal path reduces the mean error of matrix-vector multiplication by 79% compared to an offline calibration method. These results highlight diagonal path properties as a practical and scalable solution for calibrating and programming reconfigurable multiport interferometers.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".