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Record W2087207307 · doi:10.1103/physreva.87.022105

Beable-guided quantum theories: Generalizing quantum probability laws

2013· article· en· W2087207307 on OpenAlexaff
Adrian Kent

Bibliographic record

VenuePhysical Review A · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsQuantum processTheoretical physicsPhysicsQuantumOpen quantum systemQuantum probabilityQuantum algorithmQuantum operationQuantum dynamicsQuantum mechanicsStatistical physicsClassical mechanics

Abstract

fetched live from OpenAlex

Beable-guided quantum theories (BGQT) are generalizations of quantum theory, inspired by Bell's concept of beables. They modify the quantum probabilities for some specified set of fundamental events, histories, or other elements of quasiclassical reality by probability laws that depend on the realized configuration of beables. For example, they may define an additional probability weight factor for a beable configuration, independent of the quantum dynamics. Beable-guided quantum theories can be fitted to observational data to provide foils against which to compare explanations based on standard quantum theory. For example, a BGQT could, in principle, characterize the effects attributed to dark energy or dark matter, or any other deviation from the predictions of standard quantum dynamics, without introducing extra fields or a cosmological constant. The complexity of the beable-guided theory would then parametrize how far we are from a standard quantum explanation. Less conservatively, we give reasons for taking suitably simple beable-guided quantum theories as serious phenomenological theories in their own right. Among these are the possibility that cosmological models defined by BGQT might in fact fit the empirical data better than any standard quantum explanation, and the fact that BGQT suggest potentially interesting nonstandard ways of coupling quantum matter to gravity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.035
GPT teacher head0.317
Teacher spread0.282 · 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 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

Citations19
Published2013
Admission routes1
Has abstractyes

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