发表机构
University of Oxford; Aegiq Ltd.; AWE（牛津大学; Aegiq有限公司; 英国原子能管理局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨张量网络量子方法用于粒子网格代码，以克服等离子体模拟瓶颈，并支持任意位置电磁场插入，演示激光加速等离子体。
AI 中文摘要
求解控制等离子体丰富物理的高维非线性偏微分方程仍然是一个重大的计算挑战。在流体动力学中，基于张量网络技术的量子启发方法近来已成为一种有前景的新范式，在传统硬件上提供高度数据压缩，并为在量子计算机上求解指数级规模的问题提供了自然途径。早期研究表明，张量网络方法也可能有助于克服计算等离子体动力学中的关键瓶颈，特别是在传统模拟成本高昂的工业相关场景中。在本工作中，我们展示了与标准粒子网格代码相比，张量网络方法发展的最新进展，并讨论了其克服传统方法瓶颈的能力。我们进一步扩展了张量网络公式，以支持在任意空间位置插入电磁场。该能力通过注入激光场加速等离子体得到演示，为基于张量网络的受外部驱动等离子体系统模拟提供了关键一步。
英文摘要
Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge. In fluid dynamics, quantum-inspired methods based on tensor-network techniques have recently emerged as a promising new paradigm, offering large degrees of data compression on conventional hardware and a natural pathway towards solving exponentially large problems on quantum computers. Early studies suggest that tensor-network methods may also help overcome key bottlenecks in computational plasma dynamics, particularly for industry-relevant scenarios where conventional simulations remain costly. In this work, we present recent progress in developing tensor-network methods compared to standard particle-in-cell codes and discuss their capability to overcome the bottlenecks of conventional approaches. We further extend the tensor-network formulation to support the insertion of electromagnetic fields at arbitrary spatial locations. This capability is demonstrated through the injection of a laser field to accelerate a plasma, providing a key step towards tensor-network-based simulation of externally driven plasma systems.
Comments4 pages, 7 figures. Accepted Workshop Paper (QC4PDE) at IEEE Quantum Week 2026
