Oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution
Bibliographic record
Abstract
An on-demand source of bright entangled photon pairs is desirable for quantum key distribution (QKD) and quantum repeaters. The leading candidate to generate entangled photon pairs is based on spontaneous parametric down-conversion (SPDC) in a non-linear crystal. However, a fundamental trade-off exists between entanglement fidelity and efficiency in SPDC sources due to multiphoton emission at high brightness, which limits the pair extraction efficiency to 0.1% when operating at near-unity fidelity. Quantum dots in photonic nanostructures can in principle overcome this trade-off; however, the quantum dots that have achieved an en- entanglement fidelity on par with an SPDC source (99%) have poor pair extraction efficiency of 0.01%. Here, we show a measured peak concurrence of 95.3% ± 0.5% and pair extraction efficiency of 0.65% from an InAsP quantum dot in an InP photonic nanowire waveguide. Additionally, we show that an oscillating two-photon Bell state generated by a semiconductor quantum dot can establish a secure key for peer-to-peer QKD while using all generated photon pairs. Using our time-resolved QKD scheme alleviates the need to remove the exciton fine structure splitting.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".