Problem
Research questions and friction points this paper is trying to address.
Dynamic Circuits
Mid-circuit Measurements
Classical Simulation
Tensor Network Simulation of Dynamic Circuits
🤖 AI Summary
本文通过扩展基于DMRG的方法并引入两种策略来解决动态电路经典模拟中的指数级分支增长问题，为动态电路的模拟提供了一个实用且可扩展的框架。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tensor Network
Dynamic Circuits
DMRG
Single-Path Stochastic
Multi-Path
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