论文标题
模拟烟囱量子处理器上的具有挑战性的相关分子和材料
Simulating challenging correlated molecules and materials on the Sycamore quantum processor
论文作者
论文摘要
模拟复杂分子和材料是量子设备的预期应用。在人工任务中证明了强大的量子优势，我们研究了这种优势如何转化为建模相关电子结构的物理问题。我们在源自Google的Sycamore架构的超导量子处理器上模拟静态和动力学的电子结构，这些量子处理器针对两个代表性相关的电子问题：氮酶铁 - 硫酸分子簇和Trichloride $α$ -Ruthenium trichloride，一种准确的自旋材料。为此，我们将电子结构简化为适合设备上的低能性自旋模型。通过大量的错误减轻错误和经典模拟数据的帮助，我们实现了在类似体系结构上使用人工量子优势实验中使用的大约1/5的大约1/5的栅极资源的有意义的结果。当选择适合硬件的模型时，这将增加到栅极资源的1/2以上。我们的工作将人工量子优势的度量转换为与物理相关的环境。
Simulating complex molecules and materials is an anticipated application of quantum devices. With strong quantum advantage demonstrated in artificial tasks, we examine how such advantage translates into modeling physical problems of correlated electronic structure. We simulate static and dynamical electronic structure on a superconducting quantum processor derived from Google's Sycamore architecture for two representative correlated electron problems: the nitrogenase iron-sulfur molecular clusters, and $α$-ruthenium trichloride, a proximate spin-liquid material. To do so, we simplify the electronic structure into low-energy spin models that fit on the device. With extensive error mitigation and assistance from classically simulated data, we achieve quantitatively meaningful results deploying about 1/5 of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over 1/2 of the gate resources when choosing a model that suits the hardware. Our work serves to convert artificial measures of quantum advantage into a physically relevant setting.
