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Record W1586638331 · doi:10.1109/iscas.2005.1465139

Performance Comparison of Quantum-dot Cellular Automata Adders

2005· article· en· W1586638331 on OpenAlexafffund
Rumi Zhang, Konrad Walus, Wei Wang, G.A. Jullien

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsUniversity of CalgaryWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsAdderComputer scienceCarry (investment)Quantum dot cellular automatonCarry-save adderCellular automatonSerial binary adderLatency (audio)ArithmeticParallel computingElectronic engineeringAlgorithmMathematicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

QCA adders are fundamental building blocks of complex QCA computational units. In our analysis, we show that QCA ripple-carry adder and bit-serial adder designs actually outperform carry-look-ahead and carry-select adder designs. The QCA ripple-carry and bit-serial adders require fewer clocking zones, which have been found to be the main contributing factor toward overall circuit latency.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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
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

Citations91
Published2005
Admission routes2
Has abstractyes

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