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Record W4401306751 · doi:10.1103/ymjl-hpm4

Information flow-enhanced precision in collisional quantum thermometry

2025· preprint· en· W4401306751 on OpenAlexaff
Taysa M. Mendonça, Diogo O. Soares-Pinto, Mauro Paternostro

Bibliographic record

VenuePhysical Review Research · 2025
Typepreprint
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsQueen's University
FundersHORIZON EUROPE Framework ProgrammeDepartment for the EconomyFundação de Amparo à Pesquisa do Estado de São PauloConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean CommissionEngineering and Physical Sciences Research CouncilLeverhulme Trust
KeywordsInformation flowFlow (mathematics)QuantumComputer sciencePhysicsMechanicsQuantum mechanicsPhilosophy

Abstract

fetched live from OpenAlex

We describe and analyze a quantum thermometer based on a multilayered collisional model. We propose a qubit system whose architecture provides significant sensitivity even for short interaction times between the ancillae that compose the thermometer probe and the system to be probed. The assessment of the flow of information taking place within the layered thermometer and between system and thermometer reveals that the tuning of the mutual backflow of information has a positive influence on the precision of thermometry, and helps unveiling the information-theoretic mechanisms behind the working principles of the proposed architecture.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.058
GPT teacher head0.464
Teacher spread0.406 · 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.

Study designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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