张量网络用于一维以上格点规范理论：路线图
Tensor Networks for Lattice Gauge Theories beyond one dimension: a Roadmap
AI总结：
本文综述张量网络方法在格点规范理论中的最新进展，提出算法发展路线图，并估算攻克大规模格点规范理论所需的理论与计算资源。
摘要：
张量网络方法是一类基于定制变分波函数来研究平衡态和非平衡态多体量子系统的数值工具和算法。它们在模拟格点规范理论方面找到了重要应用，涉及高能物理中的相关问题。与蒙特卡洛方法相比，它们不受符号问题的影响，从而能够探索诸如有限化学势和实时动力学等具有挑战性的区域。为了应对基本挑战，如获取连续极限或大规模量子色动力学的计算，需要进一步的发展。在这项工作中，我们回顾了张量网络方法的最新进展，并讨论了算法发展的可能路线图以及增强其能力并将其应用扩展到开放高能问题的策略。我们为攻克大规模格点规范理论提供了理论和计算资源扩展的定制估算。
英文摘要：
Tensor network methods are a class of numerical tools and algorithms to study many-body quantum systems in and out of equilibrium, based on tailored variational wave functions. They have found significant applications in simulating lattice gauge theories approaching relevant problems in high-energy physics. Compared to Monte Carlo methods, they do not suffer from the sign problem, allowing them to explore challenging regimes such as finite chemical potentials and real-time dynamics. Further development is required to tackle fundamental challenges, such as accessing continuum limits or computations of large-scale quantum chromodynamics. In this work, we review the state-of-the-art of Tensor Network methods and discuss a possible roadmap for algorithmic development and strategies to enhance their capabilities and extend their applicability to open high-energy problems. We provide tailored estimates of the theoretical and computational resource scaling for attacking large-scale lattice gauge theories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
