Bibliographic record
Abstract
Breakthrough developments in quantum information technology is paving the way to the Quantum Internet. This article aims to provide an overview of the current state of research and development in the field of Quantum Communications and Quantum Internet. We examine the main building blocks and the latest achievements. By providing a holistic overview of recent achievements, this article fosters a deeper understanding of these building blocks, the related opportunities, and challenges associated with the realization of the Quantum Internet. We also provide some simple pedagogical models aimed to give the reader a clear insight on the interactions between quantum networking elements. A simple Quantum Internet 5Level-3D communication model is also provided, which integrates with, rather than parallels, the classical OSI and TCP/IP models. We further describe deployed testbeds and trials and highlight potential opportunities and challenges for Communication Service Provider's. We also provide some KPIs of quantum technologies and their evolution over the last decade. This article is intended as a self-contained entry level tutorial for newcomers who are not familiar with quantum information theory, but with some background knowledge about classical networking. Academics, students, as well as professionals can use this article to become able to begin researching the Quantum Internet, without having to read a plethora of articles, web pages, and books on the subject.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".