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On solving for shocks and travelling waves using a quantum algorithm

2025· article· en· W4406972291 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.

Bibliographic record

VenueComputers & Fluids · 2025
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsTrinity College
Fundersnot available
KeywordsAlgorithmQuantumComputer scienceMathematicsApplied mathematicsPhysicsQuantum mechanics

Abstract

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In this paper, we solve for shocks and travelling waves in advection , inviscid Burgers’ and Burgers’ equations by implementing a recently established quantum algorithm in the literature. The quantum algorithm has been successful in solving Navier–Stokes, flow generated by Burgers’ and submarine tephra flow equations under certain initial and boundary conditions . Here, we further study the efficacy of the quantum algorithm by extending the application to advection , inviscid Burgers’ and Burgers’ equations under different kinds of initial and boundary conditions. In addition to central differencing and upwinding, Lax–Wendroff discretization scheme has also been introduced in the quantum algorithm to observe how numerical dissipation and dispersion are affected. We recover known travelling waves, and shocks with rarefaction and expansion.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.941
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.014
GPT teacher head0.258
Teacher spread0.245 · 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