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Record W4321082215 · doi:10.1103/prxquantum.4.010317

Density Matrix Renormalization Group with Tensor Processing Units

2023· article· en· W4321082215 on OpenAlexfundno aff
Martin Ganahl, Jackson Beall, Markus Hauru, Adam G. M. Lewis, Tomasz Wojno, Jae Hyeon Yoo, Yijian Zou, Guifré Vidal

Bibliographic record

VenuePRX Quantum · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum many-body systems
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueVerily Life SciencesGovernment of CanadaInstitut de Ciències FotòniquesCanadian Institute for Advanced Research
KeywordsDensity matrix renormalization groupAnsatzMatrix product stateRenormalization groupMatrix multiplicationTensor productHamiltonian (control theory)Tensor (intrinsic definition)ScalingPhysicsIsing modelMathematicsQuantum mechanicsQuantumPure mathematicsGeometryMathematical optimization

Abstract

fetched live from OpenAlex

A novel architecture is used to boost the density-matrix renormalization group, an important and widely used algorithm, impressively pushing the boundary of the current state of the art by at least an order of magnitude in complexity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.007

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 head0.017
GPT teacher head0.255
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations35
Published2023
Admission routes1
Has abstractyes

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