The Quantum What? Advantage, Utopia or Threat?
Bibliographic record
Abstract
Quantum computing is at the top of the agenda for several countries. They acknowledge the strategic importance of it. They invest significant public funds in the development of this technology. While some show unconditional enthusiasm, others are more moderate and even very critical with respect to the promises of quantum computing. It is not easy to navigate for a non-expert in the field. Does quantum computing have a real advantage or is it rather a utopia? Moreover, is quantum computing, as they say, a real threat to computer and Internet security? This article takes the point of view of non-experts and attempts to shed light on these questions. In turn, we consider quantum computing as an advantage, a utopia, or a security threat. We will briefly look at the applications that we think are the most promising. Then, we review the different efforts made by the participants engaged in the race for the quantum computer. Finally, we try to project ourselves into the future.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".