Cryptographic and Non-Cryptographic Network Applications and Their Optical Implementations
Bibliographic record
Abstract
The use of quantum mechanical signals in communication opens up the opportunity to build new communication systems that accomplishes tasks that communication with classical signals structures cannot achieve. Prominent examples are Quantum Key Distribution Protocols, which allows the generation of secret keys without computational assumptions of adversaries. Over the past decade, protocols have been developed that achieve tasks that can also be accomplished with classical signals, but the quantum version of the protocol either uses less resources, or leaks less information between the involved parties. The gap between quantum and classical can be exponential in the input size of the problems. Examples are the comparison of data, the scheduling of appointments and others. Until recently, it was thought that these protocols are of mere conceptual value, but that the quantum advantage could not be realized. We changed that by developing quantum optical versions of these abstract protocols that can run with simple laser pulses, beam-splitters and detectors. [1–3] By now the first protocols have been successfully implemented [4], showing that a quantum advantage can be realized. The next step is to find and realize protocols that have a high practical value.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".