MétaCan
Menu
Back to cohort
Record W3151992605 · doi:10.1109/mdat.2021.3069138

Adaptive Integer Linear Programming Model for Optimal Qubit Permutation

2021· article· en· W3151992605 on OpenAlexaff
Alexandre A. A. de Almeida, Gerhard W. Dueck

Bibliographic record

VenueIEEE Design and Test · 2021
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsUniversity of New Brunswick
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPermutation (music)Integer programmingQubitLinear programmingInteger (computer science)Computer scienceQuantumDiscrete mathematicsMathematicsAlgorithmPhysicsQuantum mechanicsProgramming language

Abstract

fetched live from OpenAlex

Editor's notes: Quantum computer performance is often limited by the maximum number of pairs of qubits that can interact. This article expresses the problem in terms of integer linear programming, showing that in this way we can identify an optimal solution. -Marcelo Lubaszewki, Universidade Federal do Rio Grande do Sul -Matteo Sonza Reorda, Politecnico di Torino.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Design and TestSame topicQuantum Computing Algorithms and ArchitectureFrench-language works237,207