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Record W2148541058 · doi:10.1109/pacrim.2009.5291282

Generating Toffoli networks from ESOP expressions

2009· article· en· W2148541058 on OpenAlexaff
Yasaman Sanaee, Gerhard W. Dueck

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsToffoli gateComputer scienceBenchmark (surveying)HeuristicSet (abstract data type)AlgorithmCascadeQuantum gateTheoretical computer scienceQuantumQubitArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper a new heuristic ESOP-based synthesis method for Toffoli networks is proposed. This synthesis method takes advantage of the shared-ESOP cubes among the outputs to generate a cascade of Toffoli gates. The method is suitable to generate circuits for functions with large number of input variables. A greedy approach is utilized to select pairs among the different outputs with common ESOP-cubes. The shared terms need only to be realized once and the result can then be transferred to the second output, and thus reducing the number of Toffoli gates. The synthesis algorithm has been applied to a set of benchmark functions. Experimental results show that the algorithm can generate circuits with reduced quantum cost for virtually all functions. Template applications can further improve the results.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 teacher head, 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

Citations22
Published2009
Admission routes1
Has abstractyes

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