对于具有远程合的300个被困离子量子位的哈密尔顿式学习
Shi-An Guo1, Yu-Kai Wu1,2,3, Jing Ye1
1Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.
Science advances
|January 29, 2025
概括
研究人员在大型离子陷量子模拟器中开发了一种有效的方法来学习量子哈密尔顿数. 这一突破克服了一个关键的挑战,使这些强大的量子设备能够有定量应用.
科学领域:
- 量子模拟的量子模拟
- 量子多体物理学 量子多体物理学
- 量子信息科学 量子信息科学
背景情况:
- 量子模拟器为研究复杂的量子模型提供了潜力,而这些复杂的量子模型对于经典计算机来说是难以处理的.
- 学习模拟的哈密尔顿式对于定量应用至关重要,但在扩展和准确性方面面临挑战.
- 目前的噪音中等尺度量子 (NISQ) 设备缺乏高保真度通用门操作,阻碍了哈密尔顿式学习.
研究的目的:
- 在一个大规模的二维离子陷量子模拟器上演示有效的哈密尔顿式学习.
- 为了克服与学习量子系统中的哈密尔顿式相关的时间和资源缩放挑战.
- 为了实现大规模量子模拟器的定量应用.
主要方法:
- 使用了一个300量子位的二维离子陷量子模拟器.
- 采用全球操纵和单量子比特解析状态检测用于哈密尔顿式学习.
- 通过配合无和的陷潜力,开发了一个物理指导的学习方案.
主要成果:
- 成功学习了所有对所有合的伊斯模型哈密尔顿式.
- 通过量子资源实现了高效的学习,量子资源与量子位数线性扩展.
- 使用指导式学习方案证明了独立于系统大小的量子样本复杂性.
结论:
- 开发的哈密尔顿学习方法对于大型离子陷量子模拟器来说是高效和可扩展的.
- 这项工作解决了利用量子模拟器进行科学发现的关键瓶.
- 在探索量子多体物理中为大型离子陷量子模拟器的广泛应用铺平了道路.
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