Towards Real‐World Quantum Networks: A Review
Bibliographic record
Abstract
Abstract Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology, and fundamental tests. One of the key challenges for implementing a quantum network is to distribute entangled flying qubits to spatially separated nodes, at which quantum interfaces or transducers map the entanglement onto stationary qubits. The stationary qubits at the separated nodes constitute quantum memories realized in matter while the flying qubits constitute quantum channels realized in photons. Dedicated efforts around the world for more than 20 years have resulted in both major theoretical and experimental progress toward entangling quantum nodes and ultimately building a global quantum network. Here, the development of quantum networks and the experimental progress over the past two decades leading to the current state of the art for generating entanglement of quantum nodes based on various physical systems such as single atoms, cold atomic ensembles, trapped ions, diamonds with nitrogen‐vacancy centers, and solid‐state host doped with rare‐earth ions are reviewed. Along the way, the merits are discussed and the potential of each of these systems toward realizing a quantum network is compared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".