发表机构
Washington University in Saint Louis; Oak Ridge National Laboratory（华盛顿大学圣路易斯分校; 橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用张量网络技术规模化含噪声的哈密顿学习，给出误差解析界并模拟验证其稳定性，实现N=300自旋链的混合场伊辛模型哈密顿量学习，为相关实验提供经验。
AI 中文摘要
学习量子系统的哈密顿量对于理解和控制其动力学至关重要，近年来已成为广泛关注的课题。为了理解学习协议的误差容限，即其在存在不可避免误差时的稳定性，本研究利用张量网络技术在有噪声的情况下大规模模拟哈密顿学习工作流。具体而言，我们采用基于近似定态的哈密顿学习技术，该定态通过矩阵乘积工具构建。我们给出了哈密顿学习估计误差的解析界，并进行了数值模拟，结果表明学习误差在两类误差下具有稳定性。利用我们的工作流，我们能够将该协议规模化，学习N=300个格点的自旋链的混合场伊辛模型哈密顿量。通过大规模模拟该协议并实证研究其实际局限，我们对误差如何影响哈密顿学习过程的分析为未来实验提供了宝贵经验。最后，我们讨论了本研究开辟的途径以及可支持哈密顿学习实验的未来工作。
英文摘要
Learning a quantum system's Hamiltonian is crucial for understanding and controlling its dynamics and has recently become a topic of widespread interest. To understand the learning protocol's error tolerances, i.e. its stability in the presence of inevitable errors, this work utilizes tensor network techniques to emulate Hamiltonian learning workflows at scale and with noise. Specifically, we employed a Hamiltonian learning technique based on approximate stationary states which are constructed using matrix product tools. We provide analytic bounds on the Hamiltonian learning estimation errors and perform numerical simulations that highlight learning error's stability under two families of errors. Using our workflow, we are able to scale up the protocol and learn mixed-field Ising model Hamiltonians of an $N=300$ site spin chain. By simulating the protocol at large scale, and empirically studying its practical limitations, our analysis of how errors affect the Hamiltonian learning process provides valuable lessons for future experiments. We conclude by discussing the avenues our work opens as well as future work that can support Hamiltonian learning experiments.
Comments7 pages, 3 figures, comments welcome
