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arXiv 2602.05978quant-ph
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改进的Rodeo算法性能用于谱函数和态制备

Improved Rodeo Algorithm Performance for Spectral Functions and State Preparation

University of Colorado Boulder(科罗拉多大学博尔德分校);University of Michigan(密歇根大学);Michigan State University(密歇根州立大学)

Matthew Patkowski, Onat Ayyildiz, Katherine Hunt, Nathan Jansen, Dean Lee

AI总结:

改进Rodeo算法通过几何级数时间采样提升谱函数和态制备性能

摘要:

Rodeo算法是一种用于计算哈密顿量能量谱和准备其能量本征态的量子计算方法。我们讨论如何改进Rodeo算法在这些两个应用中的性能。特别是,我们证明使用几何级数的时间采样提供了一个给定总运行时间的近最优优化空间,通过在代表有间隙多体量子系统的模型哈密顿量上研究Rodeo算法的性能。分析解释了这种时间采样的性能以及保持Rodeo算法已建立的指数性能的条件。最后,我们在此采样协议上展示了各种物理哈密顿量,证明了其实际应用性。我们的结果表明,几何级数的时间提供了一种实用的、近最优的且稳健的时间采样策略,用于Rodeo算法在各种哈密顿量上的量子态制备,而无需特定模型的微调。

英文摘要:

The Rodeo Algorithm is a quantum computing method for computing the energy spectrum of a Hamiltonian and preparing its energy eigenstates. We discuss how to improve the performance of the rodeo algorithm for each of these two applications. In particular, we demonstrate that using a geometric series of time samples offers a near-optimal optimization space for a given total runtime by studying the Rodeo Algorithm performance on a model Hamiltonian representative of gapped many-body quantum systems. Analytics explain the performance of this time sampling and the conditions for it to maintain the established exponential performance of the Rodeo Algorithm. We finally demonstrate this sampling protocol on various physical Hamiltonians, showing its practical applicability. Our results suggest that geometric series of times provide a practical, near-optimal, and robust time-sampling strategy for quantum state preparation with the Rodeo Algorithm across varied Hamiltonians without requiring model-specific fine-tuning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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