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Record W4415452623 · doi:10.32370/ia_2025_03_4

Overview of Artificial Intelligence and Quantum Computer Projects

2025· article· W4415452623 on OpenAlexvenueno aff
Сергей Воронин

Bibliographic record

VenueIntellectual Archive · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsQuantum computerQuantumQuantum technologyField (mathematics)Presentation (obstetrics)Quantum informationAdaptation (eye)

Abstract

fetched live from OpenAlex

"Quantum supremacy" is such a level of performance of a quantum computer that it provides solutions to problems requiring practically unattainable power from conventional supercomputers. As of today, quantum supremacy is achieved only on some model tasks by several quantum computers, but the development of quantum computing is going on all over the world. The development of quantum computing includes not only the creation of quantum computers, but also the creation of infrastructure for access to computing power, the development of software, and the capability to use quantum computers together with conventional supercomputers. The countries in which these laboratories have been formed have become leaders in the field of quantum computing. Quantum computer laboratories focus on the development, modeling, integration, and adaptation of practical applications for the new quantum computer. The presentation of the first quantum computer is an important milestone. This is not a one-off initiative, but part of a broad strategy for promoting breakthrough technologies in various fields. All these efforts are part of an international strategic plan for the development of quantum computing aimed at maintaining the technological leadership of developers, preserving the competitiveness of high-tech industry, and ensuring the sustainable economic growth of smart technologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0020.002
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0230.010

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.

Opus teacher head0.149
GPT teacher head0.328
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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