New sum-of-squares factorization method reduces matrix-model circuit costs by 3.8–11.9× in T gates and 25.5× in surface-code spacetime. Works on both NISQ and fault-tolerant quantum computers with verified circuits and proven lower bounds.
#QuantumCompilation #MatrixModels #QuantumSimulation
#QuantumCompilation #MatrixModels #QuantumSimulation
Efficient Compilation of Matrix-Model Quantum Circuits Using Sum-of-Squares Factorization for NISQ and Fault-Tolerant Hardware
arxiv.org
October 6, 2026 at 1:28 AM
New sum-of-squares factorization method reduces matrix-model circuit costs by 3.8–11.9× in T gates and 25.5× in surface-code spacetime. Works on both NISQ and fault-tolerant quantum computers with verified circuits and proven lower bounds.
#QuantumCompilation #MatrixModels #QuantumSimulation
#QuantumCompilation #MatrixModels #QuantumSimulation
Algorithms cut partition‑based cross‑validation to the time of a single XᵀX/XᵀY product. Published in Journal of Chemometrics 2025 and on arXiv:2401.13185. Read more: https://getnews.me/fast-partition-based-cross-validation-algorithms-for-matrix-models/ #crossvalidation #matrixmodels #chemometrics
September 29, 2025 at 6:42 PM
Algorithms cut partition‑based cross‑validation to the time of a single XᵀX/XᵀY product. Published in Journal of Chemometrics 2025 and on arXiv:2401.13185. Read more: https://getnews.me/fast-partition-based-cross-validation-algorithms-for-matrix-models/ #crossvalidation #matrixmodels #chemometrics
New on CRAN: glm4 (0.1.0). View at https://CRAN.R-project.org/package=glm4
glm4: Fitting Generalized Linear Models Using Sparse Matrices
Fits Generalised Linear Models (GLMs) with sparse and dense 'Matrix' matrices for memory efficiency. Acts as a wrapper for the glm4() function in the 'MatrixModels' package <<a href="https://doi.org/10.32614%2FCRAN.package.MatrixModels" target="_top">doi:10.32614/CRAN.package.MatrixModels</a>>, but adds convenient model methods and functions designed to mimic those associated with the glm() function from the 'stats' package.
CRAN.R-project.org
April 16, 2026 at 10:31 AM
New on CRAN: glm4 (0.1.0). View at https://CRAN.R-project.org/package=glm4
Updates on CRAN: betategarch (3.4), causalweight (1.1.3), CytOpT (0.9.6), eCerto (0.8.4), lpda (1.2.0), MatrixModels (0.5-4), metaRNASeq (1.0.8), proteus (1.1.5), summarytools (1.1.3), survexp.fr (1.2)
March 26, 2025 at 1:32 PM
Updates on CRAN: betategarch (3.4), causalweight (1.1.3), CytOpT (0.9.6), eCerto (0.8.4), lpda (1.2.0), MatrixModels (0.5-4), metaRNASeq (1.0.8), proteus (1.1.5), summarytools (1.1.3), survexp.fr (1.2)
New preprint! Your feedback is welcomed.
“Solving three core challenges in transient dynamics analysis of matrix population models”
A new framework for analyzing transient dynamics.
doi.org/10.1101/2025.05.29.656837
#PopulationEcology #TheoreticalEcology #MatrixModels #EcologicalModeling
“Solving three core challenges in transient dynamics analysis of matrix population models”
A new framework for analyzing transient dynamics.
doi.org/10.1101/2025.05.29.656837
#PopulationEcology #TheoreticalEcology #MatrixModels #EcologicalModeling
Solving three core challenges in transient dynamics analysis of matrix population models
1. Populations are at the mercy of random disturbances large and small and rarely, if ever, converge on predicted long-term behaviours. Therefore, when using matrix population models, ecologists stud...
doi.org
June 5, 2025 at 12:42 AM
New preprint! Your feedback is welcomed.
“Solving three core challenges in transient dynamics analysis of matrix population models”
A new framework for analyzing transient dynamics.
doi.org/10.1101/2025.05.29.656837
#PopulationEcology #TheoreticalEcology #MatrixModels #EcologicalModeling
“Solving three core challenges in transient dynamics analysis of matrix population models”
A new framework for analyzing transient dynamics.
doi.org/10.1101/2025.05.29.656837
#PopulationEcology #TheoreticalEcology #MatrixModels #EcologicalModeling