论文标题
高能量物理学的张量网络:对雪质的贡献2021
Tensor networks for High Energy Physics: contribution to Snowmass 2021
论文作者
论文摘要
张量网络方法对于高能物理学,凝结物理学和量子信息科学(QIS)变得越来越重要。我们讨论了在高能物理(HEP)的背景下,张量网络方法对晶格场理论,量子重力和QI的影响。这些工具将针对强烈相互作用的系统的计算,当使用常规的蒙特卡洛和其他重要性抽样方法时,这些工具会因符号问题而困难。将需要进一步开发方法和软件,以对HEP产生重大影响。我们讨论了未来几年执行量子染色体动力学(QCD)相关计算的路线图。这项研究是劳动密集型的,需要最先进的计算科学和计算机科学的开发和验证。我们简要讨论与其他科学领域和行业的重叠。
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.