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Record W4404465506 · doi:10.1103/physrevb.110.184414

High-fidelity dimer excitations using quantum hardware

2024· article· en· W4404465506 on OpenAlexaff
Norhan M. Eassa, Joe Gibbs, Zoë Holmes, Andrew Sornborger, Łukasz Cincio, Gavin Hester, Paul Kairys, Mário Motta, Jeffrey F. Cohn, Arnab Banerjee

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsBrock University
FundersOak Ridge National LaboratoryU.S. Department of Energy
KeywordsHigh fidelityFidelityDimerComputer scienceComputer hardwarePhysicsEngineeringElectrical engineeringNuclear magnetic resonanceTelecommunications

Abstract

fetched live from OpenAlex

A spin dimer is the drosophila of an entangled system and the building block for more complex states such as valence bond solids. Trotterization on noisy quantum devices cannot yet cleanly yield the dynamics of quantum systems over large time scales. Here, the authors use a combination of a direct measurement scheme and variational fast forwarding to mitigate the need for large circuit depths from several trotter steps, thereby yielding a class-leading fidelity for the dimer dynamical structure factor on IBM quantum hardware.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.368
Teacher spread0.344 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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