坚定地学习超导量子处理器的哈密尔顿动力学.
Dominik Hangleiter1,2,3, Ingo Roth4,5, Jonáš Fuksa6
1Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland and NIST, College Park, MD, USA. mail@dhangleiter.eu.
Nature communications
|November 6, 2024
概括
我们开发了一种新方法,使用超导量子比特精确描述模拟量子模拟器的特征. 这种技术准确地估计了哈密尔顿参数,并识别了错误,这对于推进量子计算至关重要.
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.
- 超导量子比特系统 超导量子比特系统
背景情况:
- 对模拟量子模拟器进行准确的表征对于实现超越经典计算能力至关重要.
- 超导量子比特是构建量子模拟器的领先平台,但精确的哈密尔顿参数估计仍然具有挑战性.
研究的目的:
- 为超导量子比特模拟器开发一个可扩展和强大的哈密尔顿学习算法.
- 从时间序列数据中精确估计自由哈密尔顿参数,即使存在状态准备和测量 (SPAM) 错误.
主要方法:
- 一个可扩展的哈密尔顿式学习算法,可以对抗SPAM错误.
- 一种新的超分辨率技术,tensorESPRIT,用于从矩阵时间序列中提取频率.
- 对参数估计进行受约束的多元组优化.
主要成果:
- 精确估计最多14个超导量子比特的哈密尔顿参数,精度低于MHz.
- 获得了关于SPAM错误的断层信息.
- 为27量子比特网格构建了一个空间实现错误地图.
结论:
- 开发的工具包可以准确地描述模拟量子处理器的特征.
- 这项工作促进了量子模拟器的理解,校准和改进.
- 这些发现对于开发能够进行复杂计算的量子模拟器至关重要.
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