AI 中文总结
研究大规模量子优化问题，提出随机泡利路径模拟器（SPPS），通过泡利路径采样实现无偏随机梯度估计，经理论分析和多基准测试评估，能跟踪优化动态、快速收敛，扩展了基于泡利模拟在量子优化中的作用。
AI 中文摘要
基于泡利的模拟器为低魔法态量子电路的大规模经典模拟提供了一条有前景的途径。然而，它们的适用性很大程度上仍限于正向模拟，不足以用于如变分态制备和参数初始化等优化驱动的量子任务。现有方法要么缺乏对基于梯度优化的原生支持，要么存在严重的梯度偏差。本文提出随机泡利路径模拟器（SPPS），这是一个用于大规模量子优化的计算框架，通过在优化迭代中进行泡利路径采样实现无偏随机梯度估计。理论分析表明该模拟器能产生无偏梯度估计并具有可证明的收敛保证。通过对多达100个量子比特的量子本征求解器基准测试和多达40个量子比特的量子神经网络基准测试进行系统评估，SPPS能忠实地跟踪优化动态，在数分钟内收敛，并将基于泡利的模拟从正向估计扩展到大规模量子优化。
英文摘要
Pauli-based simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime. Yet their applicability remains largely limited to forward simulation, making them inadequate for optimization-driven quantum tasks such as variational state preparation and parameter initialization. Existing approaches either lack native support for gradient-based optimization or suffer from severe gradient bias. Here we propose the stochastic Pauli-path simulator (SPPS), a computational framework for large-scale quantum optimization that enables unbiased stochastic gradient estimation via Pauli-path sampling across optimization iterations. Our theoretical analysis shows that the proposed simulator yields unbiased gradient estimates and admits provable convergence guarantees. We systematically evaluate our proposal, including quantum eigensolver benchmarks with up to 100 qubits and quantum neural network benchmarks with up to 40 qubits. Across these tasks, SPPS faithfully tracks optimization dynamics, converges within minutes, and broadens the role of Pauli-based simulation from forward estimation to large-scale quantum optimization.
Comments43 pages, 8 figures
