Multi-Transverse-Mode Silicon Photonics for Quantum Computing
Bibliographic record
Abstract
Photonics has been identified as a highly promising platform for optical classical and quantum computing. In the realm of classical computing, the inherent parallelism of optics, as opposed to the sequential operations of electronics, offers significant potential for achieving faster and more energy-efficient computational capabilities. Photonics also represents a highly advantageous option for the pursuit of integrated quantum computing, primarily due to the noise- and decoherence-free nature of single photons. Optical qubits can be effectively generated by encoding single photons in a given degree of freedom, such as polarization, path, or wavelength. Path encoded programmable quantum gates have already been realized in Silicon Photonics (SiPh), a technology platform offering several appealing features such as compatibility with complementary metal oxide semiconductor (CMOS) for high integration density. To achieve a large-scale photonic quantum system, encoding the information on other degrees of freedom, such as polarization and transverse mode, is essential. Here, we present an open access process design kit (PDK) for multi-transverse-mode components compatible with standard 220 nm thick SiPh technology. Then, we discuss the use of the PDK components in developing multi-transverse-mode classic optical computing. We also investigate multiple transverse modes of light for encoding and manipulating information in quantum photonics.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".