Kerr Combs and Telecommunications Components for the Generation and High-Dimensional Quantum Processing of d-Level Cluster States
Bibliographic record
Abstract
Large and complex optical quantum states are a key resource for fundamental science and applications such as quantum communications, information processing, and metrology. In this context, cluster states are a particularly important class because they enable the realization of universal quantum computers by means of the so-called `one-way' scheme, where processing operations are performed through measurements on the state. While two-level (i.e. qubit) cluster states have been realized thus far, further boosting this computational resource by increasing the number of particles comes at the price of significantly reduced coherence time and detection rates, as well as increased sensitivity to noise. In contrast, the realization of d-level (with d > 2) cluster states offers the possibility to increase quantum resources without changing the number of particles, enables the implementation of efficient computational protocols, as well as coincides with a reduction in the noise sensitivity of the states. Here, we experimentally realize, characterize, and perform one-way processing operations on three-level, four-partite cluster states formed by two photons in the time and frequency domain. We make use of a unique approach based on integrated photonic chips and optical fiber communications components, enabling scalable, deterministic new functionalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".