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Record W4415500119 · doi:10.1007/978-3-031-90731-9_13

Shor Algorithm

2025· book-chapter· en· W4415500119 on OpenAlexaff
Ray LaPierre

Bibliographic record

Venue˜The œMaterials Research Society series · 2025
Typebook-chapter
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsMcMaster University
Fundersnot available
KeywordsQuantum algorithmQuantum phase estimation algorithmQuantumQuantum computerBasis (linear algebra)Prime (order theory)Algorithm designSet (abstract data type)

Abstract

fetched live from OpenAlex

In 1994, a mathematician named Peter Shor surprised the world by publishing an efficient quantum algorithm for finding the prime factors of very large numbers, which is the basis for today’s data encryption. This discovery spurred the further development of quantum computing, making Shor’s algorithm one of the most important algorithms.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0530.032

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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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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