Preprint
Tensor networks for High Energy Physics: contribution to Snowmass 2021
03/09/2022
DOI: 10.48550/arxiv.2203.04902
Abstract
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.
Details
- Title: Subtitle
- Tensor networks for High Energy Physics: contribution to Snowmass 2021
- Creators
- Yannick MeuriceJames C Osborn - Argonne National LaboratoryRyo Sakai - Syracuse UniversityJudah Unmuth-Yockey - Fermi National Accelerator LaboratorySimon Catterall - Syracuse UniversityRolando D Somma - Los Alamos Medical Center
- Resource Type
- Preprint
- DOI
- 10.48550/arxiv.2203.04902
- Language
- English
- Date posted
- 03/09/2022
- Academic Unit
- Physics and Astronomy
- Record Identifier
- 9984442021202771
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