学习多体哈密尔顿数与海森伯格有限缩放
Hsin-Yuan Huang1, Yu Tong1,2, Di Fang2,3
1Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California 91125, USA.
Physical review letters
|June 2, 2023
概括
我们开发了一个新的量子算法,以有效地学习交互的N-量子比特哈密尔顿数. 这种方法实现了海森堡极限,大大减少了准确的哈密尔顿式学习所需的实验数量和时间.
科学领域:
- 量子信息科学 量子信息科学
- 量子计算是一种量子计算.
- 多体物理多体物理
背景情况:
- 从它的动力学中学习一个多体哈密尔顿式在物理学中至关重要.
- 现有的学习N-量子比特哈密尔顿算法的算法往往是低效的,需要许多实验和长时间的进化.
研究的目的:
- 提出第一个实现海森堡极限的算法来学习交互的N-量子比特局部哈密尔顿式.
- 开发一种高精度估计哈密尔顿参数的高效方法.
主要方法:
- 使用量子模拟技术将N量子比特哈密尔顿式解成非相互作用的补丁.
- 采用量子增强的分裂与征服策略来学习哈密尔顿式.
- 分析算法对实验错误的稳定性及其资源需求.
主要成果:
- 拟议的算法估计了N-量子比特哈密尔顿式中的任何参数在O{\displaystyle O} 总进化时间后,以高概率对 ε误差进行估计.
- 它只需要多逻辑实验,这是对现有方法的显著改进.
- 一个匹配的下界被证明,建立了开发算法的非对称最佳性.
结论:
- 开发的算法代表了学习量子哈密尔顿的突破,提供了前所未有的效率和准确性.
- 它对常见的实验不完美是坚固的,不需要特殊的量子状态.
- 这项工作为更高效的量子模拟和量子机器学习应用铺平了道路.
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