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Record W4296716006 · doi:10.18280/mmep.090404

State-Domain Equations and Their Quantum Computing Solution Based HHL Algorithm

2022· article· en· W4296716006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsQuantum computerDomain (mathematical analysis)Computer scienceComputationQuantum algorithmQuantumAlgorithmState (computer science)MathematicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Quantum computing is becoming a major new field in engineering. Adaptation of classical computation concepts to this emerging technological reality is underway. In this study, a sampling-based approach is used to adapt state-domain equations to HHL-based formulation to obtain a homologous quantum algorithm for solving the dynamic equations in the state domain. The results obtained by the quantum computing approach are very close to the real response of the system and to the power-series-based exact solution. This work represents a significant contribution to the advancement of dynamic systems modeling and solving in state-domain adapted for use in quantum computers in the future.

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.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.023
GPT teacher head0.202
Teacher spread0.179 · 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