MétaCan
Menu
Back to cohort
Record W2245224556 · doi:10.1103/physrevx.6.031016

Milestones Toward Majorana-Based Quantum Computing

2016· article· en· W2245224556 on OpenAlexafffund

Bibliographic record

VenuePhysical Review X · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of British Columbia
FundersInstitute for Quantum Information and Matter, California Institute of TechnologyWalter Burke Institute for Theoretical PhysicsVetenskapsrådetVillum FondenAspen Center for PhysicsCrafoordska StiftelsenDanmarks GrundforskningsfondCalifornia Institute of TechnologyMicrosoft ResearchDivision of Materials ResearchNatur og Univers, Det Frie ForskningsrådGordon and Betty Moore FoundationNational Research FoundationNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationDanmarks Frie ForskningsfondNational Science Foundation
KeywordsQuantum computerSet (abstract data type)MilestoneQuantumReading (process)Quantum informationQuantum information processingQuantum information science

Abstract

fetched live from OpenAlex

Preparing, manipulating, and reading out Majorana zero modes is important for quantum computing. A theoretically developed set of milestone experiments, if conducted successfully, may pave the way for fault-tolerant ``topological'' quantum information processing.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.006
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.028
GPT teacher head0.313
Teacher spread0.285 · 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
GenreEmpirical

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

Citations484
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review XSame topicTopological Materials and PhenomenaFrench-language works237,207