Long-Lived Solid-State Optical Memory for High-Rate Quantum Repeaters
Bibliographic record
Abstract
We argue that long optical storage times are required to establish entanglement at high rates over large distances using memory-based quantum repeaters. Triggered by this conclusion, we investigate the $795.325\text{ }\text{ }{\mathrm{nm}}^{3}$ ${\mathrm{H}}_{6}\ensuremath{\leftrightarrow}{^{3}\mathrm{H}}_{4}$ transition of $\mathrm{Tm}:{\mathrm{Y}}_{3}{\text{Ga}}_{5}{\mathrm{O}}_{12}$ (Tm:YGG). Most importantly, we find that the optical coherence time can reach 1.1 ms, and, using laser pulses, we demonstrate optical storage based on the atomic frequency comb protocol during up to $100\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$ as well as a memory decay time ${T}_{m}$ of $13.1\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$. Possibilities of how to narrow the gap between the measured value of ${T}_{m}$ and its maximum of $275\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$ are discussed. In addition, we demonstrate multiplexed storage, including with feed-forward selection, shifting and filtering of spectral modes, as well as quantum state storage using members of nonclassical photon pairs. Our results show the potential of Tm:YGG for creating multiplexed quantum memories with long optical storage times, and open the path to repeater-based quantum networks with high entanglement distribution rates.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".