在量子多体系统中使用经验差异信息减少能量估计的采样复杂性
Alexander Gresch1,2, Uğur Tepe1, Martin Kliesch2
1Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany.
Journal of chemical theory and computation
|July 21, 2025
概括
本研究引入了一种自适应量子能量估计方法,可以提高采样复杂性的保证. 这种新的方法,使用经验性的伯恩斯坦停止,与基本方法相比,提高了高达一个数量级的准确性.
科学领域:
- 量子计算是一种量子计算.
- 量子化学是一种量子化学.
- 计算物理学的计算物理.
背景情况:
- 精确的能量估计对于量子算法至关重要,特别是在量子化学中.
- 目前使用保利分解和霍夫丁不等式的方法提供有限的抽样复杂性保证.
- 优化采样复杂性对于高效的量子状态准备至关重要.
研究的目的:
- 开发一个更有效的适应式估计器,用于量子状态准备能量.
- 通过利用该州的实际差异来改进现有的一次性估计器.
- 为新的估计方法提供严格的尾部界限.
主要方法:
- 基于经验性的伯恩斯坦停止 (EBS) 算法构建了一个自适应估计器.
- 利用分组方案和国家实证差异进行估计.
- 提供严格的尾部界限,利用经验差异为理论保证.
主要成果:
- 适应式EBS估计器在基本的读数保证上持续改进.
- 在数值基准指标中表现出高达1个数量级的改善.
- 成功估计了各种哈密尔顿的基本状态能量,并提高了准确性.
结论:
- 开发的自适应估计器为量子能量的估计提供了在采样复杂性的显著改进.
- 使用EBS利用经验差异提供了一个强大的,更有效的方法.
- 这种方法对量子算法在量子化学等领域的进步有着强烈的影响.
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