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Machine Learning Reduces Variance in Lattice QCD Calculations

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Researchers have developed a new methodology using machine-learned normalizing flows to reduce variance in lattice gauge field theory calculations. This approach encodes the generating functional, enabling the systematic creation of noiseless estimators for correlation functions. The technique was demonstrated on Quantum Chromodynamics and Yang-Mills theory, achieving up to a three-orders-of-magnitude reduction in variance for glueball correlation functions and Wilson loops. AI

RANK_REASON Academic paper detailing a new methodology for variance reduction in lattice QCD calculations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine Learning Reduces Variance in Lattice QCD Calculations

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Academic paper detailing a new methodology for variance reduction in lattice QCD calculations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ryan Abbott, Yang Fu, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-L\'opez, Phiala E. Shanahan ·

    Learning the generating functional for variance reduction in lattice QCD

    arXiv:2606.15986v1 Announce Type: cross Abstract: The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. We present a methodology that leverages machine-learned norma…

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