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Record W4401883841 · doi:10.1103/physreva.110.022223

Decoherence of a two-path system by infrared photons

2024· article· en· W4401883841 on OpenAlexafffund
Colby DeLisle, P. C. E. Stamp

Bibliographic record

VenuePhysical review. A/Physical review, A · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Electrodynamics and Casimir Effect
Canadian institutionsPacific Institute for the Mathematical SciencesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum decoherencePhotonPhysicsQuantumPath (computing)InterferometryQuantum opticsQuantum informationPath integral formulationInfraredQuantum mechanicsTheoretical physicsComputer science

Abstract

fetched live from OpenAlex

We calculate the decoherence caused by photon emission for a charged particle traveling through an interferometer; the decoherence rate gives a quantitative measure of how much ``which-path'' quantum information is gained by the electromagnetic field. We isolate the quantum information content of both leading and subleading soft photons, and show that it can be extracted entirely from information about the end points of the particle's paths. When infrared dressing is used to cure the infrared divergences in the theory, the leading-order soft photons then give no contribution to decoherence, and carry no quantum information. The subleading soft photons in contrast may carry finite which-path information, and the subleading contribution to decoherence takes an extremely simple, time-independent form depending only on the size of the interferometer. An interesting open question is whether or not dressing should also be applied at subleading order; we discuss the possibility of answering this question experimentally.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.341
Teacher spread0.335 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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