2022/03/09 by Yannick Meurice, James C. Osborn, Meurice, Yannick +9
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Computational Physics and Python Applications #Computer science #Context (archaeology) #Distributed and Parallel Computing Systems #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #High Energy Physics - Theory (hep-th) #Mathematics #Monte Carlo method #Particle physics #Physics #Quantum Monte Carlo #Quantum chromodynamics #Quantum field theory #Quantum mechanics #Scientific Computing and Data Management #Statistical physics #Statistics #Tensor (intrinsic definition) #Theoretical physics #hep-lat #hep-th
paper · pdf · doi:10.48550/arxiv.2203.04902
Contribution to Snowmass 2021, preliminary version
arxiv created 2022/03/09 · openalex publication_date 2022/03/09 · arxiv updated 2022/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Tensor network methods are becoming increasingly important for high-energy physics, condensed matter physics and quantum information science (QIS). We discuss the impact of tensor network methods on lattice field theory, quantum gravity and QIS in the context of High Energy Physics (HEP). These tools will target calculations for strongly interacting systems that are made difficult by sign problems when conventional Monte Carlo and other importance sampling methods are used. Further development of methods and software will be needed to make a significant impact in HEP. We discuss the roadmap to perform quantum chromodynamics (QCD) related calculations in the coming years. The research is labor intensive and requires state of the art computational science and computer science input for its development and validation. We briefly discuss the overlap with other science domains and industry.