发表机构
Technical University of Munich; University of Pisa（慕尼黑工业大学; 比萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于张量网络的动态电路经典模拟方法，通过扩展DMRG算法并引入单路径与多路径两种分支处理策略，在保持精度的同时实现高效模拟。
AI 中文摘要
动态电路结合了中途测量和经典控制操作，扩展了量子程序的表达能力，是量子纠错、态制备和分布式量子算法等应用的核心。然而，由于中途测量导致的执行分支指数增长，其经典模拟颇具挑战。在本工作中，我们通过扩展一种基于DMRG的量子电路模拟方法以支持动态电路操作，开发了一种用于模拟动态电路的张量网络方法。为应对执行分支的指数级激增，我们引入并比较了两种策略：一种是对单个测量结果进行采样的单路径随机方法，以及一种维持分支集合的多路径方法。我们在标准动态电路（包括隐形传态协议和GHZ态制备）以及随机动态电路上对这些方法进行了基准测试。我们的分析探讨了模拟精度与计算效率之间的权衡。这些结果为动态电路的经典模拟提供了一个实用且可扩展的框架。
英文摘要
Dynamic circuits, which incorporate mid-circuit measurements and classically controlled operations, extend the expressive power of quantum programs and are central to applications such as quantum error correction, state preparation, and distributed quantum algorithms. However, their classical simulation is challenging due to the exponential growth of execution branches induced by mid-circuit measurements. In this work, we develop a tensor network approach for simulating dynamic circuits by extending a DMRG-based method for quantum circuit simulation to support dynamic circuit operations. To address the exponential proliferation of execution branches, we introduce and compare two strategies: a single-path stochastic approach that samples individual measurement outcomes, and a multi-path approach that maintains an ensemble of branches. We benchmark these methods on standard dynamic circuits, including the teleportation protocol and GHZ state preparation, as well as on random dynamic circuits. Our analysis explores the trade-offs between simulation accuracy and computational efficiency. These results provide a practical and scalable framework for the classical simulation of dynamic circuits.
CommentsAccepted at CASCON 2026 conference
