Large-scale simulations of Floquet physics on near-term quantum computers

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Timo Eckstein, Refik Mansuroglu, Piotr Czarnik, Jian-Xin Zhu, Michael J. Hartmann, Lukasz Cincio, Andrew T. Sornborger, Zoë Holmes
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Abstract

Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum’s trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency ω but also a linear advantage in simulation time t compared to Trotterization.

Abstract Image

在近期量子计算机上对 Floquet 物理进行大规模模拟
周期驱动的量子系统表现出多种多样的现象,但模拟起来比模拟平衡的量子系统更具挑战性。在这里,我们介绍量子高频浮凸模拟(QHiFFS)算法,作为在量子硬件上模拟快速驱动量子系统的一种方法。QHiFFS 的核心是 "踢算子 "的概念,它将系统转换为一个由与时间无关的有效哈密顿支配动力学的基础。这样,先前的时间无关模拟方法就可以用于模拟 Floquet 系统。我们使用周期性驱动的双轴近邻伊辛(BNNNI)模型作为案例研究来说明我们的算法,该模型是量子受挫磁性和临界性的天然试验台。我们在 Quantinuum 的困离子量子计算机上对驱动型二维 BNNNI 模型进行了 20 量子位模拟。我们的误差分析表明,与特罗特化相比,QHiFFS 不仅在驱动频率 ω 方面具有立方优势,而且在模拟时间 t 方面也具有线性优势。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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