Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
复制标题
大规模非平衡量子动力学的可扩展哈密顿学习
DOI：
10.1103/physreva.105.023302
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发表时间：
2021-03
影响因子：
2.9
通讯作者：
Agnes Valenti;Guliuxin Jin;J. L'eonard;S. Huber;E. Greplova
中科院分区：
文献类型：
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作者：
Agnes Valenti;Guliuxin Jin;J. L'eonard;S. Huber;E. Greplova
大规模量子设备提供了超越经典模拟的见解。然而，为了进行可靠和可验证的量子模拟，量子设备的构建模块需要精确的基准测试。由于缺乏有效的模拟工具，大规模动态量子系统的基准测试是一个重大挑战。在这里，我们提出了一个可扩展的算法，基于神经网络的哈密顿层析成像的非平衡量子系统。我们说明了我们的方法使用一个模型的前沿量子模拟平台：超冷原子在光学晶格。具体来说，我们表明，我们的算法是能够重建的哈密顿量的任意大小的准一维玻色子系统使用一个可访问量的实验测量。我们能够显着提高以前已知的参数精度。
Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.