Hangleiter Dominik, Roth Ingo, Fuksa Jonáš, Eisert Jens, Roushan Pedram
Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland and NIST, College Park, MD, USA.
Joint Quantum Institute (JQI), University of Maryland and NIST, College Park, MD, USA.
Nat Commun. 2024 Nov 6;15(1):9595. doi: 10.1038/s41467-024-52629-3.
Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors. The key subroutines are a novel super-resolution technique for frequency extraction from matrix time-series, tensorESPRIT, and constrained manifold optimization. Our learning results verify the Hamiltonian dynamics on a Sycamore processor up to sub-MHz accuracy, and allow us to construct a spatial implementation error map for a grid of 27 qubits. Our results constitute an accurate implementation of a dynamical quantum simulation that is precisely characterized using a new diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.
精确表征模拟量子模拟器的方法是开发能够进行超越经典计算的量子模拟器的关键。在此，我们从多达14个量子比特的测量时间序列数据中精确估计超导量子比特模拟量子模拟器的自由哈密顿量参数。为实现这一点，我们开发了一种可扩展的哈密顿量学习算法，该算法对态制备和测量（SPAM）误差具有鲁棒性，并能产生有关这些SPAM误差的断层扫描信息。关键子程序包括一种用于从矩阵时间序列中提取频率的新型超分辨率技术、张量ESPRIT以及约束流形优化。我们的学习结果验证了在Sycamore处理器上的哈密顿动力学，精度可达亚兆赫兹，并使我们能够构建一个27量子比特网格的空间实现误差图。我们的结果构成了一个动态量子模拟的精确实现，该模拟使用一个新的诊断工具包进行精确表征，以理解、校准和改进模拟量子处理器。
